https://www.sepehr.org/ Scientific- Research Quarterly of Geographical Data (SEPEHR) en daily 1 Fri, 20 Feb 2026 00:00:00 +0330 Fri, 20 Feb 2026 00:00:00 +0330 https://www.sepehr.org/article_726162.html Extended AbstractIntroductionThis study provides a comprehensive analysis of the relationship between vegetation indices and chlorophyll variation at different growth stages of rice fields in northern Iran, employing the latest advancements in remote sensing and precision agriculture technologies. Mineral nutrition plays a crucial role in plant growth and development, significantly influencing the yield and quality of rice crops. Understanding the dynamics of chlorophyll and its spatial distribution is crucial for optimizing fertilization practices, improving crop productivity, and ensuring sustainable agricultural practices.Materials & MethodsIn this research, a multifaceted approach that combined data from Sentinel-2 satellite imagery was utilized, UAV (Unmanned Aerial Vehicle) imagery equipped with RGB sensors, and ground-based SPAD (Soil Plant Analysis Development) measurements. Sentinel-2 imagery data is known for its high-resolution multispectral capabilities, which allow for detailed monitoring of vegetation health and land use changes over large geographical areas. The satellite's ability to capture multiple spectral bands enhances our capacity to assess various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), which is widely used to evaluate plant health and growth conditions. UAV imagery complements the satellite data by providing high-resolution, fine-scale detail that can be captured at specific times during the crop's growth cycle. The flexibility of UAVs enables targeted data collection, allows monitoring critical growth stages and variations in chlorophyll levels that may not be detectable through satellite imagery alone. By integrating both data sources, it is aimed to generate a comprehensive understanding of the relationship between chlorophyll and vegetation indices across different spatial and temporal dimensions. The primary objective was to develop predictive models for NDVI, a key indicator of vegetation health that correlates closely with chlorophyll concentration and fertilization need in plants. To achieve this, three machine learning algorithms: Random Forest Regression (RFR), Support Vector Regression (SVR), and Multi-Layer Perceptron Regression (MLPR) was employed. Each algorithm offers unique strengths in handling complex datasets and capturing non-linear relationships, which are common in agronomic data.Results & DiscussionThe results of analysis indicated that the RFR algorithm outperformed the other two models, achieving a correlation coefficient of 0.80 when predicting NDVI from UAV imagery. This strong correlation suggests that RFR effectively captures the intricate relationships between the spectral reflectance values obtained from UAV images and the underlying nutrition content in the rice fields. The high predictive accuracy of the RFR model highlights its potential for practical applications in precision agriculture, where timely and accurate assessments of crop health are essential for informed decision-making. In addition to predicting NDVI, the Kriging spatial interpolation technique was utilized to generate detailed chlorophyll distribution maps based on the SPAD data collected from the field. Kriging is a powerful geostatistical method that allows for optimal estimation of unmeasured locations based on observed data, providing insights into spatial variations in chlorophyll across the rice fields. The generated chlorophyll maps revealed significant correlations with NDVI, confirming that remote sensing techniques can effectively monitor nutrient dynamics and assess the overall health of crops. The findings of this research underscore the potential of integrating UAV and satellite data through machine learning techniques and advanced image processing methods for resource management in agriculture. By providing farmers with precise information regarding chlorophyll levels and vegetation health, these technologies enable more informed decision-making processes. For instance, farmers can optimize fertilization strategies by applying mineral nutrition only where it is needed and in the appropriate amounts, thereby maximizing crop yield while minimizing environmental impacts. The integration of remote sensing and precision agriculture technologies can contribute to broader goals of sustainable agriculture. As the global population continues to rise, the demand for food production increases, necessitating innovative approaches to enhance agricultural productivity while conserving natural resources.ConclusionIn conclusion, this study illustrates the effective use of remote sensing and machine learning technologies in analyzing the relationship between vegetation indices and chlorophyll variation in rice fields. The successful prediction of NDVI using the RFR algorithm, alongside the generation of chlorophyll distribution maps through Kriging, highlights the potential of these methods to enhance agricultural practices. Further research is needed to explore the applicability of these techniques across different crops and regions, paving the way for broader implementation of precision agriculture strategies. Ultimately, this research contributes to the growing body of knowledge on sustainable agricultural practices, emphasizing the role of technology in supporting farmers and promoting efficient resource management. By fostering greater collaboration between researchers, agricultural practitioners, and technology developers, the field of precision agriculture can advance and address the challenges faced by modern farming in a rapidly changing world. https://www.sepehr.org/article_736565.html Extended AbstractIntroductionChanges in the frequency distribution of temperature are among the most important manifestations of climate variability. Such changes not only reveal the nature of global warming but also, due to temperature’s undeniable interaction with other environmental variables, illuminate the root causes of many other environmental changes. Contrary to the common belief that changes in mean temperature are the primary indicator of warming, recent research shows that small changes in the mean stem from profound alterations in the temperature frequency distribution. Furthermore, statistical extreme value theory indicates that the frequency of temperature extremes depends more on the variance (scale parameter) of the distribution than on its mean (location parameter). Therefore, understanding changes in climate variance is as valuable as tracking changes in means, because the economic costs imposed by temperature variability far exceed those from mean temperature changes. For a vast country like Iran, with remarkable geographical and climatic diversity, examining changes in temperature frequency distribution can reveal regional and local patterns. This study aims to analyze the evolution of Iran’s minimum temperature frequency distribution over the past five decades (1975–2024).Materials and MethodsTwo data types were used: reanalysis and station data. The reanalysis dataset comprises hourly 2‑meter air temperature from ERA5‑Land, with hourly temporal resolution and 0.1° spatial resolution (200×150 cells) over a 50‑year period (21 March 1975 – 20 March 2025). Of 30351 total pixels within the framework, 28023 were data‑bearing (over land and inland water bodies) and 15577 pixels fell within Iran’s political borders. Daily minimum temperature was extracted as the minimum of a 24‑hour vector (from 21:00 UTC of the previous day to 20:00 UTC of the current day). Station data consisted of daily minimum temperatures from 389 Iranian meteorological stations with varying record lengths (10 to over 73 years); 143 stations with >30 years of overlap with reanalysis data were used for validation. For frequency distribution analysis, the temperature range -37 to +40°C was divided into 77 bins of 1‑degree interval. Annual minimum temperature frequencies per pixel per bin were counted and converted to relative frequency percentages. A centered array of relative frequencies (subtracting the spatial weighted mean of each column) with dimensions 15577×3850 was created. To identify spatial and temporal patterns, Singular Value Decomposition (SVD) was applied. Orthogonal left singular vectors represent spatial patterns, and right singular vectors represent temporal patterns of the temperature bins. Reanalysis validation was performed by calculating bias (reanalysis minus station) and comparing frequency distributions. The mean bias for daily minimum temperature was -0.2°C (slight underestimation).Results and DiscussionThe results show that over the past five decades, Iran’s minimum temperatures have mostly ranged between -15 and +30°C, with 15°C being the most frequent. The peaks of maximum frequency have intensified since 1998. Comparing the frequency distributions of 1982 and 2023 shows that 1982 had a bimodal distribution (temperatures near zero in the cold season and near 16°C in the warm season), whereas 2023 exhibits a more unimodal distribution with a 4% frequency peak, causing the 2023 mean minimum temperature to be about 2.9°C warmer than that of 1982. The first component of SVD explained nearly 42% of the total data variance. Its scores partition Iran into two main parts: Cold Iran (positive scores, including the mountainous northwest, Zagros, and Alborz) and Warm Iran (negative scores, including plains, lowlands, and southern coasts). The first eigenvector revealed two important temperature ranges: a cold range (-7 to +3°C) and a warm range (20–29°C). The best representative of the dominant pattern is Mount Hezar southwest of Rayen (Kerman) with a bimodal distribution on -9 and -3°C, while the opposite pattern is best represented by the Gulf of Oman coast northeast of Chabahar with a bimodal distribution on 26 and 17°C. Trend analysis of the first eigenvector shows that in Cold Iran, the frequency of temperatures below -5°C has decreased over the past five decades, replaced by increased frequency of temperatures from -1 to +8°C. In Warm Iran, the frequency of temperatures above 27°C has increased and that of temperatures 15–25°C has decreased. Comparing the second half of the period (2000–2024) with the first half (1975–1999) shows that the reduction in <‑5°C temperatures is stronger in the Sefidrud, Urmia, Aras, Karun, and Gavkhuni basins, while the increase in >27°C temperatures has occurred along the southern coasts and central Iran.ConclusionThe frequency distribution of minimum temperature in Iran has evolved over the past five decades. This evolution, in both the cold and warm parts of the country, means a rightward shift of the frequency distribution curve: decreased frequency of low temperatures and increased frequency of high temperatures. An 1.8% decrease in the frequency of temperatures below -5°C and a 1.1% increase in temperatures above 27°C have respectively raised Iran’s mean minimum temperature by 0.19°C and 0.33°C. Changes in other temperature bins (-1 to 8°C and 15–25°C) have contributed -0.04°C and +0.42°C, respectively. Overall, these changes have increased Iran’s mean minimum temperature in the second half of the period by 0.94°C relative to the first half. The largest increase in minimum temperature (more than 0.7°C) occurred in the Sefidrud, Urmia, Aras, Karun, and Gavkhuni basins. This research demonstrates that analyzing temperature frequency distribution changes is a more fundamental approach than merely studying means or extremes, and can provide environmental planners and water resource managers with a more precise picture of Iran’s climate evolution. https://www.sepehr.org/article_732716.html Extended AbstractIntroductionTropical cyclones are among the most destructive natural hazards in the North Indian Ocean, particularly in the Bay of Bengal. The intensification of these systems is critically influenced by air-sea interactions, specifically the exchange of surface latent heat flux (SLHF). SLHF represents the heat transfer associated with the phase change of water, exchanged between the Earth's surface and the atmosphere. An increase in SLHF correlates with a rise in the number and intensity of cyclone systems, as these systems derive their energy from the ocean. Sea surface temperature (SST) is a crucial indicator that influences climate patterns and significantly affects the development and intensity of tropical cyclones. Given that the Bay of Bengal is prone to the formation of many tropical cyclones, the aim of this study is to investigate the advection of surface latent heat flux, sea surface temperature and wind speed in tropical cyclone Amphan (May 2020) and tropical cyclone Nivar (November 2020). Tropical cyclone Amphan and tropical cyclone Nivar occurred in the pre-monsoon and post-monsoon periods, respectively. The use of reanalysis data to investigate surface latent heat flux (SLHF), sea surface temperature (SST), and wind speed, which play a crucial role in the formation and evolution of tropical cyclones, can be significant.Materials and MethodsThe Bay of Bengal is recognized as one of the world's most active basins for tropical cyclone formation, with two primary seasons for cyclone development: pre-monsoon (March, April, and May) and post-monsoon (October, November, and December). This research focuses on two significant events in 2020: Tropical Cyclone Amphan, which occurred from May 16 to 21 and was classified as a super cyclonic storm, and Tropical Cyclone Nivar, which developed from November 22 to 27 and was categorized as a very severe cyclonic storm by the Indian Meteorological Department (IMD). To investigate the interaction between SLHF, SST and Wind Speed during these two tropical cyclones, reanalysis data were utilized. SLHF data were obtained from the ERA5 reanalysis, which provides hourly estimates with a spatial resolution of 0.25° × 0.25°. For SST and wind speed, MERRA-2 reanalysis data were employed, offering a temporal resolution of 1 hour and a spatial resolution of 0.625° × 0.5°. All data were processed and analyzed using Python. The main track positions of the Amphan and Nivar tropical cyclones were extracted from IMD reports and IBTrACS data, and their tracks were mapped using ArcMap.Results and DiscussionThe analysis of the two tropical cyclones revealed distinct temporal and spatial variations in sea surface temperature (SST), surface latent heat flux (SLHF), and wind speed during their development and intensification stages. Tropical Cyclone Amphan formed on 16 May 2020 under SST values exceeding 30°C, which provided a favorable environment for rapid intensification. SLHF increased sharply from approximately 250 W/m² to more than 300 W/m² and reached values above 400 W/m² on 19 May, coinciding with Amphan’s peak intensity. Wind speeds on 19 May, 15 m/s. Subsequently, as the cyclone approached land, SST decreased, leading to weakening. In contrast, Tropical Cyclone Nivar developed on 22 November 2020 with lower SST values (around 29°C). SLHF exhibited a gradual increase, reaching its maximum (300–400 W/m²) on 25 November. Wind speeds intensified to about 12.5 m/s. Overall, the results demonstrate that pre-monsoon conditions favor stronger heat flux exchange and more intense cyclone development compared to the post-monsoon period. ConclusionThis study investigated the interaction between surface latent heat flux (SLHF), sea surface temperature (SST), and wind speed during the evolution of Tropical Cyclone Amphan (pre-monsoon) and Tropical Cyclone Nivar (post-monsoon) in the Bay of Bengal. The results show that pre-monsoon conditions create a more favorable environment for cyclone intensification due to higher SST and stronger heat exchange. During Amphan’s peak on 19 May, SLHF 400 W/m², SST reached about 31°C, and wind speed increased to nearly 15 m/s. In contrast, during Nivar’s peak on 25 November, SLHF ranged between 300 and 400 W/m², SST decreased to 26–29°C, and wind speed reached approximately 12.5 m/s. Overall, the findings of 2020 indicate that the interaction between latent heat flux, sea surface temperature, and wind speed has a time-dependent nature, and the seasonal differences play a significant role in the intensity and evolution of tropical cyclones. This study practically demonstrates that seasonal variations in surface latent heat flux (SLHF), sea surface temperature (SST), and wind speed can enhance the accuracy of tropical cyclone forecasts in the Bay of Bengal. https://www.sepehr.org/article_735695.html Extended Abstract Introduction Coastal environments are among the most productive yet highly sensitive ecosystems on Earth, playing a critical role in sustaining livelihoods, food security, biodiversity, and human well-being. Estimates indicate that approximately 60% of the world’s population resides in coastal zones; areas that, while attracting industrial, tourism, and infrastructure-related activities, simultaneously experience the greatest intensity of human pressures. In the absence of integrated management and effective governance, such pressures can lead to the degradation of coastal ecosystems, the erosion of social capital, and a decline in the quality of life of local communities. Qeshm Island, as the largest island in the Persian Gulf and one of Iran’s first trade–industrial free zones, exemplifies this situation. Established to promote economic development, attract investment, and improve local livelihoods, the island has, over recent decades, faced a range of environmental, social, economic, and institutional challenges. The aim of this study is to identify and prioritize development threats on Qeshm Island and to provide an analytical framework for elucidating the roots of unsustainability while proposing appropriate management strategies. Materials and Methods This study employed a mixed-methods (qualitative–quantitative) approach and was conducted in seven stages. In the first stage, initial threats were identified through a review of documents and previous studies. These threats were then refined and supplemented through semi-structured interviews with the local community. Subsequently, a structured questionnaire was developed, and the identified issues were prioritized using the Eisenhower Importance–Urgency Matrix. In the following stages, in-depth interviews with experts and local specialists were conducted to collect the necessary data for analyzing causal relationships among the threats. The DEMATEL technique was employed to examine the cause–effect structure, and finally, the results were integrated within the DPSIR framework (Drivers–Pressures–State–Impact–Response) to provide a systemic representation of the unsustainability cycle on Qeshm Island. This methodological integration enabled the simultaneous identification of the intensity, direction, and nature of relationships among the threats. Results The study identified approximately 30 threat factors on Qeshm Island, of which 15 were classified as “important and urgent” based on the Importance–Urgency Matrix. These threats were primarily associated with drivers such as unsustainable tourism development, weak infrastructure development, insufficient economic development, and managerial inefficiency. The DEMATEL analysis revealed that institutional and managerial threats—particularly “managerial duality between the Free Zone Organization and the governorate,” “frequent turnover of managers,” “absence of monitoring and evaluation systems,” and “unaccountable governance”—belong to the causal group and play the most significant role in shaping other threats. In contrast, factors such as environmental degradation, cultural erosion, development lacking social attachment, and the decline of social capital were the most affected by other threats. Discussion and Conclusion Comparing DEMATEL results with the DPSIR framework revealed that the pattern of unsustainability on Qeshm Island is neither linear nor simple; rather, it operates as an institutional–social feedback loop. Weak governance and an inefficient managerial structure act as the primary drivers, generating pressures in economic, social, and environmental domains. These pressures undermine the island’s ecological and social conditions, leading to consequences such as environmental degradation, social dissatisfaction, and a decline in social capital. Subsequently, these very consequences, by eroding public trust and participation, further reproduce governance weaknesses and institutional inefficiencies. These findings indicate that unsustainable development on Qeshm Island does not stem from a lack of natural resources or economic capacity, but rather from a flawed governance structure and the absence of institutional coordination. The findings of this study also indicate that the classical DPSIR framework alone is insufficient to fully explain the institutional and social dynamics in free zones. Integrating it with network analysis tools such as DEMATEL can provide a better understanding of nonlinear relationships, feedback loops, and the role of social capital in the unsustainability cycle. From this perspective, the present study not only offers a comprehensive depiction of the issues facing Qeshm Island but also provides a practical analytical model for examining development threats in sensitive coastal and island areas. Overall, the findings of this study emphasize that piecemeal and sectoral solutions—such as implementing infrastructure projects or controlling limited environmental pressures—will not be sufficient to address Qeshm’s challenges without reforming the underlying institutional roots. Escaping the unsustainability cycle requires reengineering the governance structure of free zones, clarifying institutional mandates, strengthening managerial accountability, enhancing local community participation in decision-making, and giving serious attention to social capital as the foundation for sustainable development. The experience of Qeshm Island can serve as a cautionary example for other free zones in the country, demonstrating that development without coordinated, transparent, and participatory governance not only fails to achieve sustainable well-being but also becomes a factor in reproducing social and environmental unsustainability. https://www.sepehr.org/article_731384.html Extended AbstractIntroduction:With the rapid global shift toward renewable energy sources, photovoltaic (PV) systems have emerged as a cornerstone for clean and sustainable power generation. Ensuring the optimal performance and long-term durability of these systems requires effective inspection and maintenance strategies. Solar panels are frequently exposed to harsh environmental factors such as dust, moisture, temperature variations, and mechanical stress, which can lead to various physical defects including cracks, hotspots, delamination, and surface contamination. If left undetected, these defects may significantly compromise the energy output and reduce the economic lifespan of the solar panel infrastructure.Traditional inspection methods, such as manual visual inspection or infrared thermography, are often labor-intensive, expensive, and prone to inaccuracies due to human error and environmental interference. These limitations underscore the need for an automated, accurate, and scalable solution. This research presents a smart defect detection framework based on deep learning, leveraging the cutting-edge YOLOv8m model for accurate and real-time identification of defects in solar panels. In addition, spatial analysis techniques are integrated to investigate the geographic distribution of defects, aiding in the development of targeted and predictive maintenance policies.Materials & Methods:The core of the proposed framework is the YOLOv8m (You Only Look Once version 8, medium variant) model, an advanced object detection architecture optimized for speed, accuracy, and efficiency. YOLOv8m is known for its compact design and superior performance, making it suitable for deployment in real-time monitoring systems.A curated dataset was constructed for training and evaluation, consisting of both colored (RGB) and grayscale images of solar panels collected under various illumination conditions, angles, and environmental settings. The images were annotated to highlight six prevalent types of defects observed in real-world installations: cracks, hot spots, delamination, bird droppings, dirt patches, and broken glass.To improve the model’s robustness and generalization capability, several data augmentation techniques were applied, such as random flipping, rotation, brightness variation, and Gaussian noise injection. Hyperparameter tuning was conducted to optimize learning rates, batch sizes, and anchor box dimensions. The model was trained using a transfer learning approach with pre-trained weights on the COCO dataset and fine-tuned on the specific solar panel defect dataset.Furthermore, to extract spatial insights, the metadata embedded within the image files—such as GPS coordinates and timestamps—was utilized. This data was combined with the defect detection results to perform spatial clustering and distribution mapping using GIS-based tools and statistical analysis techniques.Results & Discussion:The proposed YOLOv8m model achieved impressive results across several performance metrics. The model recorded a mean average accuracy (mAP) of 97.43%, a precision score of 97%, and a recall rate of 97.58% in detecting and classifying the six identified defect types. These values were consistently higher than those of traditional deep learning models such as Convolutional Neural Networks (CNN), VGG16, and ResNet50, which served as baseline comparisons. Specifically, the YOLOv8m framework demonstrated a relative improvement of 1.5% to 3.5% in detection accuracy over the baseline models, highlighting its effectiveness in handling complex visual scenarios.Qualitative analysis further supported these results, with the YOLOv8m model accurately localizing defects with tight bounding boxes and minimal false positives. The lightweight nature of the architecture enabled near real-time inference on standard GPU hardware, making it viable for deployment in field conditions.The integration of spatial analysis added a novel dimension to the defect detection task. By correlating the defect locations with their geographic coordinates, patterns such as defect clustering in specific panel zones or environmental exposure zones were identified. These patterns suggested that external factors like shading from nearby objects, accumulation of debris, or exposure to extreme weather conditions could contribute to defect formation. Such insights can be used to develop location-specific maintenance protocols or preventive interventions, thereby improving the overall health and longevity of the solar panel infrastructure.Conclusion:This research introduces a comprehensive and scalable framework for intelligent detection of solar panel defects, built on the robust and efficient YOLOv8m architecture. The system not only offers high accuracy and real-time performance but also incorporates spatial intelligence for deeper understanding of defect trends and propagation. Its performance superiority over conventional models and its operational feasibility position it as a powerful tool for smart solar farm management.By facilitating early diagnosis and enabling condition-based maintenance, the proposed framework contributes to reducing operational costs, extending the service life of solar panels, and enhancing the efficiency of photovoltaic systems. Moreover, the integration of machine vision and spatial analytics bridges the gap between technical diagnostics and actionable maintenance strategies, paving the way for smarter and more sustainable energy infrastructures. https://www.sepehr.org/article_736066.html Extended Abstract 1-IntroductionUrban development in hazard-prone regions has become a central concern in contemporary urban studies, particularly as cities face increasing pressures from both natural and human-induced challenges. The city of Izeh, located in southwestern Iran, represents a prominent example of the intersection of environmental vulnerabilities with socio-economic and infrastructural deficiencies. Its unique geographical setting—surrounded by mountainous terrain and situated near active fault lines—exposes the city to recurrent natural hazards such as earthquakes, flash floods, and land subsidence. Simultaneously, rapid and unplanned urban growth, physical deterioration, infrastructural inefficiencies, and increasing environmental pollution have intensified the city's exposure to risk. These overlapping challenges have collectively weakened urban resilience and hindered the achievement of sustainable and balanced development. In recent years, the concept of urban resilience has gained prominence as a guiding framework for understanding how cities can anticipate, absorb, and recover from shocks. However, resilience-building requires a precise understanding of the specific local-scale challenges that drive vulnerability. In the case of Izeh, despite its strategic importance and historical background, systematic research on its urban challenges remains limited. This study aims to fill this gap by providing a comprehensive assessment of the most important urban development challenges in Izeh, prioritizing them within a structured analytical framework, and proposing operational strategies tailored to local conditions. This extended abstract reviews the study's objectives, methodological approach, and key findings, and also includes conceptual diagrams and tables extracted from the full article to visually summarize the research process and outcomes. By integrating expert knowledge with public perceptions, this study offers a holistic understanding of the city's vulnerabilities. 2-Materials and Methods This study employs an applied, descriptive-analytical methodology based on a mixed quantitative-qualitative approach. The research design was structured to ensure both scientific accuracy and practical applicability, enabling a comprehensive assessment of urban development challenges in Izeh. Data collection was conducted through three primary channels: (1) an extensive review of scientific literature, urban development plans, and governmental reports; (2) expert questionnaires completed by urban planners, municipal managers, environmental specialists, and university faculty members; and (3) public questionnaires distributed among local residents and stakeholders to capture community-level perceptions. The methodological framework consisted of four sequential stages. In the first stage, a preliminary list of urban challenges was identified through literature review and field observations. These challenges were categorized into five major domains: natural hazards, physical-spatial issues, infrastructural weaknesses, environmental problems, and socio-economic challenges. In the second stage, the identified challenges were validated using the Delphi method through iterative consultations with experts. In the third stage, prioritization of the validated challenges was conducted using the Analytic Hierarchy Process (AHP). Expert Choice software was used to calculate pairwise comparisons and extract weights for each category of challenges. To increase the representativeness of the results, a relative weighting method was employed to integrate expert assessments with the results of public questionnaires. This dual approach enabled a balanced evaluation based on both technical expertise and local community concerns. Finally, the fourth stage was dedicated to formulating operational strategies based on the highest-priority challenges. These strategies were developed through content analysis of expert recommendations, a review of successful international experiences, and consideration of the local conditions of Izeh. The methodological rigor of the study ensures that the findings possess both analytical robustness and practical applicability, providing a reliable foundation for urban planning and policy-making. 3-Discussion and Results The results of the study reveal that the challenges facing urban development in Izeh are multi-dimensional and deeply interconnected. The AHP analysis indicates that infrastructural weaknesses constitute the highest-priority challenge, receiving the largest weight among all categories. These weaknesses include deficiencies in water supply systems, sewage networks, transportation infrastructure, and public service facilities. The inadequacy of these systems not only reduces citizens' quality of life but also exacerbates the consequences of natural hazards. The second-highest priority is vulnerability to natural hazards, particularly earthquakes, flash floods, and land subsidence. The city's geographical location near active fault lines and its exposure to seasonal floods significantly increase the risk of widespread damage. The lack of early warning systems and insufficient disaster preparedness further intensify this vulnerability. The third major challenge is the deteriorated urban fabric, especially in the central and older neighborhoods of the city. These areas suffer from aging buildings, narrow streets, and a lack of open spaces, making them highly susceptible to structural damage during natural disasters. Environmental issues—including air pollution, improper waste management, and degradation of natural resources—rank fourth, reflecting growing concerns about ecological sustainability. Socio-economic challenges, although ranked fifth, remain highly significant; high unemployment rates, limited economic diversification, and low levels of citizen participation hinder the implementation of sustainable development policies. 4-ConclusionThis study provides a comprehensive assessment of the most important urban development challenges in Izeh and proposes a structured framework for prioritizing these challenges. The findings indicate that infrastructural weaknesses, vulnerability to natural hazards, deteriorated urban fabric, environmental problems, and socio-economic constraints collectively shape the city's development trajectory. By integrating expert perspectives with public perceptions, the present study offers a balanced and evidence-based picture of urban vulnerabilities. The proposed operational strategies—including improving infrastructure, regenerating deteriorated neighborhoods, enhancing environmental management, strengthening governance mechanisms, and increasing citizen participation—outline practical pathways toward a more resilient and sustainable future. These strategies align with successful international experiences while being sensitive and appropriate to local conditions. Finally, the study emphasizes the importance of proactive planning and integrated governance in addressing the complexities of urban development. The prioritization framework presented can serve as an effective tool for policymakers, urban planners, and local managers in allocating resources and designing targeted interventions. By implementing the proposed strategies, Izeh can take steps toward achieving a more resilient, equitable, and sustainable urban environment. https://www.sepehr.org/article_732719.html Extended AbstractIntroductionThe concept of mass-space, defined as the interaction between built environments (mass) and open/public areas (space), plays a pivotal role in shaping sustainable and livable cities. In industrializing metropolises such as Ahvaz, unplanned urban expansion has intensified noise pollution as an environmental stressor, a issue that has thus far received limited scholarly attention. Adopting an interdisciplinary approach, this study investigates the relationship between urban morphology and sonic ecology, proposing 'sound level' as an effective diagnostic tool for identifying and analyzing mass-space patterns in Ahvaz. As a hub for Iran's oil and gas industries, Ahvaz faces significant noise pollution challenges stemming from heavy traffic, concentrated industrial activity, and deficiencies in urban planning. Through sound mapping and examining its correlation with urban form, this research demonstrates how acoustic data can contribute to more equitable planning, reduced health risks, and the preservation of urban identity. While prior studies have predominantly focused on noise modeling in Western cities, this work addresses a gap in the literature concerning Middle Eastern urban contexts.Materials and MethodsThis study employed a mixed-methods framework integrating field analysis, Geographic Information Systems (GIS), and statistical modeling. Acoustic data were collected at 300 sampling points using a stratified random sampling method and a calibrated KIMO DB100 sound level meter during two peak periods: daytime (9:00 AM) and nighttime (9:00 PM). To generate a city-wide sound level zoning map, the Inverse Distance Weighting (IDW) interpolation method was applied within ArcGIS software, with the output classified into five qualitative categories. The validity and reliability of the methodology were confirmed via a two-stage verification process: first, evaluation against independent data from 30 control points, which yielded a Mean Absolute Error (MAE) of 2.8 dB; and second, calculation of Pearson's correlation coefficient (r = 0.91) between the sound level layer and the municipal land-use map, confirming a strong spatial congruence.Results and DiscussionThe findings indicate that approximately 54% of Ahvaz's area (equivalent to over 11,600 hectares) exceeds the national permissible sound level limits. This figure underscores the considerable scale of the noise pollution problem in this metropolis. The revealed spatial pattern demonstrates a direct correlation with the city's mass-space structure. The primary noise pollution hotspots (with levels of 78-70 dB) are concentrated in the industrial zones of the southeast, heavy-traffic corridors leading to the Karun River bridges, and dense residential-commercial cores. These areas clearly correspond to the city's intensive, high-activity 'mass.' Conversely, zones with the lowest sound levels (43-35 dB) predominantly align with open 'spaces,' including vacant lands, barren areas, and green spaces in the city's west and southwest, highlighting their role as urban respiratory spaces and acoustic buffers. The robust correlation coefficient (0.91) quantitatively confirms that sound level can serve as a reliable proxy indicator for identifying the intensity of human activity and analyzing mass-space configurations. Although this finding aligns with global studies in densely populated cities, the severity of pollution in Ahvaz's industrial areas and the emergence of a pronounced polarized pattern (noisy east versus quiet west) reveal the ineffectiveness of current zoning policies and a distinct spatial inequality that disproportionately affects lower-income residents.ConclusionThis study demonstrates that sound level mapping is a powerful, cost-effective, and objective tool for diagnosing spatial inequalities and analyzing the structure of urban mass-space. The findings emphasize the urgent necessity of integrating acoustic considerations into the urban planning, design, and management processes in Ahvaz. Practical solutions are proposed at three levels: at the physical/design level, establishing green belts and buffers as acoustic insulation and revising building regulations; at the macro-policy level, reviewing zoning plans and incorporating noise standards into master documents; and at the managerial level, deploying intelligent monitoring systems and designating quiet urban areas. This research provides a framework for urban planners and designers to utilize acoustic indicators in moving towards the formation of more sustainable, equitable, and higher-quality cities. https://www.sepehr.org/article_734117.html Extended Abstract1-IntroductionOne of the hazards that is always associated with great human and financial losses is flood risk. Flood is one of the natural hazards in which human activities play an important role. In recent years, the increasing population trend has led to the development of residential areas towards the river banks, and this issue, along with land use changes and destruction of vegetation, has increased the probability and intensity of floods and the resulting damages. The increasing population trend and industrial development have led to the advancement of human societies towards the river banks and the concentration of economic activities in flood plains, and this factor has caused many urban areas to be exposed to flood risk and face billions in financial losses and human losses annually. Considering that flood risk is considered one of the challenges facing societies, it is very important to implement management measures, monitor and control land use changes, manage river courses, and identify areas prone to flooding. Different regions have different potentials for flooding, depending on the hydrogeomorphology and human factors. One of the areas at risk of flooding is the city of Nourabad in Lorestan province. The location of Nourabad city on the Badavar River and its topographic condition have made this city vulnerable to flooding, and for this reason, in recent years, including in April 2019, it has faced the risk of flooding. Given the importance of the issue, this study aims to identify areas prone to flooding and also areas flooded in Nourabad city during the April 2019 flood. 2-Materials and MethodsIn this study, a 30-meter digital elevation model, Landsat 7 and 8 satellite images, and Sentinel 1 radar images were used as the most important research data. The most important research tools were ArcGIS (to prepare the desired maps and standardize the information layers), SuperDecisions (to implement the ANP model), ENVI (to prepare land use maps), IDRISI (to implement the WLC model), and Google Earth Engine (to identify flooded areas). Considering the subject and objectives, this study was conducted in several stages. In the first stage, six parameters of elevation, distance from the river, slope, slope direction, lithology, and land use of the region were used to identify flood-prone areas, as well as WLC and ANP models. In the second stage, Google Earth Engine and Sentinel 1 images were used to identify flooded areas. In the third stage, the results obtained from zoning methods and radar images were compared. In the fourth stage, in order to evaluate the trend of development of residential areas towards flood-prone areas, Landsat 7 and 8 satellite images from 2010 and 2020 were used. 3-Discussion and ResultsThe location of Nourabad city has caused this city to have a high flood potential. In this study, in order to identify areas vulnerable to flood risk, 6 parameters of height, distance from the river, slope, slope direction, land use and lithology were used. Based on the final map, the southern and central areas of Nourabad urban area have a high flood potential due to their low height and slope, as well as proximity to the river. Also, in this study, a map of the flooded areas in April 2019 was prepared using radar images and Google Earth Engine. Based on the prepared map, a large part of the Nourabad urban area, including its southern and central areas, is facing the risk of flooding. Also, the urban periphery areas, including the areas along the main river of this city, are facing flooding. According to the results obtained, there is a correspondence between the results obtained from zoning methods and radar images. Accordingly, using the parameters and methods used in this research, areas vulnerable to flood risk can be identified with high accuracy. 4-ConclusionThe results obtained from the WLC-ANP zoning method have shown that a large part of the urban area of ​​Nourabad, including its central and southern areas, has a high flood potential due to its low elevation and slope, as well as proximity to the main river. Also, the peripheral areas of Nourabad, which are located in the vicinity of the main river, also have a high flood potential. In this study, the status of the flooded areas during the flood of April 2019 was also evaluated using radar images. Based on the results obtained, a large part of the urban area of ​​Nourabad, including the central and southern areas of Nourabad, has faced flood risk. Comparing the results obtained from the zoning methods and radar images has shown the consistency of the results obtained; accordingly, it can be concluded that using the parameters and methods used in this study, areas vulnerable to flood risk can be identified with high accuracy. Also, in this study, the process of physical development of residential areas in Nourabad city towards flood-prone areas was evaluated, and based on the results, the area of ​​residential areas in the category with very high flood potential in the years 2000 and 2020 was about 1.3 and 1.9 square kilometers, respectively. According to the results, it can be concluded that in the process of physical development of residential areas in Nourabad city, the flood potential of this city has not been taken into account, and this has led to the development of residential areas towards vulnerable areas. https://www.sepehr.org/article_711278.html Extended AbstractIntroduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis is more visible. Due to the fact that most of the rainfall falls in the form of snow in mountainous areas, the management of snow resources in these areas is very important, and knowing the different aspects of variability and geographical patterns governing the phenomenon of snow is a scientific and practical need. It is considered special in water resources and in the agricultural sector. Thus, in the current research, the spatio-temporal patterns governing the annual average of snow density in different decades and the difference of each of the decades compared to the entire time period have been estimated and analyzed using spatial statistics methods.Materials & Methods: The studied area with an area of ​​about 151,771.91 square kilometers is located between 34°44' to 39°25' north latitude from the equator and 44°3' to 49°52' east longitude from the Greenwich meridian. In order to investigate the spatial autocorrelation changes of the average snow density in northwest Iran during the years 1982-2022 from the data obtained from the database of the European Center for Medium-Range Atmospheric Forecasting ECMWF4/ ERA5 based on daily data, and to identify and understand the spatial patterns of density Barf, based on statistical and graphic models have been used in the geographic information system environment. In the study of temporal-spatial changes of the average snow density of the region in different time periods including 4 decades ((1982-1992), (1992-2002), (2002-2012), (2012-2022)) and the whole period of 41 years (2022) -1982)), general Moran's I and Getis-Ord Gi* statistics were used. Also, in the current research, in order to investigate the effect of changes in Extreme snow precipitation on the amount of snow density in the northwest region, it has been done to determine the snow threshold. In order to estimate snow drift, a threshold was defined. Since the station snowfall amount data has a high dispersion, values ​​above the mean cannot be accurate for defining the threshold of freezing snow. In this way, the 99th percentile index has been used to determine the snow threshold.Results & Discussion: The aim of the current research is to investigate the spatial autocorrelation changes of the annual mean snow density in the northwest of Iran. For this purpose, the annual snow density data during the statistical period of 1982-2022 was obtained from the ECMWF/EAR5 database with a resolution of 0.25 x 0.25 degrees, and then divided into four ten-year periods. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettys-RDJ) were used at the significance level of 90, 95 and 99%. Also, in order to investigate the effect of extreme precipitation on changes in the level of snow density, the 99th percentile statistical index was used, and based on this index, the freezing threshold of each synoptic station in the region was determined during the last decade (2012-2022) and the interval the entire statistical period (1982-2002) was carried out. The results of the present research showed that in the studied area, snow density has spatial autocorrelation and a strong cluster pattern. With a density threshold less than 0.10 kg/m3, from the first decade to the end of the fourth decade, the area (number of pixels) and the amount of snow density in the northwest have decreased. The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly during the last decade of the study, and this has caused the snow density to increase relatively in the last decade compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has significantly decreased during the last four decades.Conclusion: The evaluation of the temporal changes of snow density also strengthened the hypothesis of the occurrence of freezing snow precipitation leading to an increase in snow density in the months of cold seasons during the last decade. This point was confirmed by examining the statistical index of the 99th percentile of snowy days of each synoptic station in the region during the last decade (2009-2018) compared to the entire period of station statistics (2000-2018). The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly in the last decade of the study and this has caused the snow density in the last decade to increase relatively compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has decreased significantly during the last four decades. Moran's statistic was used to explain the pattern governing snow density in northwest Iran. The results of Moran's index about the annual average of snow density showed that the values ​​related to different time periods have a positive coefficient and are close to one, which indicates that the snow density data has spatial autocorrelation and has a cluster pattern. Also, the results of standard Z score and P-value confirmed the cluster significance of the spatial distribution of snow density in the northwest. Finally, the analysis of hot spots has been a clear confirmation of the continuation of concentration and clustering of snow density in northwest Iran in space with the increase of the time period, which mountainous areas have the first rank in the formation of hot clusters with a probability of 99%. have given. https://www.sepehr.org/article_734690.html Extended AbstractIntroductionVegetation is one of the most sensitive environmental components to changes in climatic variables, and any fluctuation in precipitation, temperature, and humidity can result in rapid and observable responses in its growth and dynamics. Teleconnection patterns, particularly ENSO (El Niño–Southern Oscillation), are recognized as major drivers of climatic conditions at regional and global scales and can substantially influence vegetation cover by altering precipitation and temperature patterns. Monitoring and modeling vegetation cover can therefore help in tracking regional climate-change trends (Jiao et al., 2018; Ding et al., 2020; Jien et al., 2023). Identifying climate-induced fluctuations in vegetation conditions is particularly important, especially given recent climate change and the role of vegetation in mitigating its impacts. The most widely used parameter for evaluating vegetation responses to climate variability is the Normalized Difference Vegetation Index (NDVI), derived from satellite remote-sensing data (Adole et al., 2016; Huang et al., 2021; Suberi et al., 2021; Buras et al., 2020; Barbosa et al., 2019). Teleconnection patterns represent persistent, large-scale atmospheric circulation regimes that influence distant regions by altering temperature, precipitation, and pressure patterns. These patterns are statistically defined and explain variations in local and regional climatic variables in response to different phases of large-scale climate modes. Among these, ENSO (El Niño–Southern Oscillation) and NAO (North Atlantic Oscillation) are the most influential in climate–biosphere studies. Jien et al. (2025), in a study titled Impact of the El Niño–Southern Oscillation on Global Vegetation, demonstrated that ENSO, through its influence on precipitation and temperature patterns, is one of the most important drivers of interannual vegetation variability worldwide. Their findings suggest that ENSO impacts differ across regions and that the type of ENSO event—whether the Eastern Pacific (EP) or Central Pacific (CP) pattern—can induce distinct vegetation responses. Nevertheless, the precise interactions between vegetation and ENSO require further investigation. A review of previous research shows that only a limited number of studies in Iran have examined the influence of teleconnection patterns on vegetation dynamics. By focusing on the Maroon Basin in the southern Zagros region and employing key teleconnection indices, the present study addresses a major research gap in this field. Materials and MethodsIn this study, to investigate the relationship between teleconnection indices (NINO4, NINO3, NINO3.4, NINO1+2, and SOI) and winter vegetation changes in the Maroon watershed, the Normalized Difference Vegetation Index (NDVI) from the MODIS sensor was used for the period 2001–2023. MODIS data, with a spatial resolution of 250 m and a 16-day temporal interval, were obtained after atmospheric correction and processed on the Google Earth Engine platform. NDVI was calculated based on the ratio (NIR − Red)/(NIR + Red). To assess the relationship, Pearson correlation coefficients were calculated between the teleconnection indices during winter and winter NDVI over the 23-year period. Following this, the locations with the highest and lowest correlation coefficients were identified. The coefficient indicates both the strength and direction of the relationship, with values close to ±1 representing a strong dependency. Teleconnection indices reflect the synchronization of climate fluctuations in a given region with changes in sea-level pressure and temperature in other regions, and their data were obtained from the NCEP/NCAR database. Additionally, the mean NDVI for each winter season was calculated in a GIS environment, as winter represents the main rainy season in the watershed. The low vegetation density during this season facilitates the detection of changes induced by climate variability and human activities. To examine the relationships, Pearson correlation coefficients were calculated between the winter teleconnection indices and the winter NDVI values over a 23-year period. After computing the coefficients, the locations with the highest and lowest correlation values were identified.Results and DiscussionThe winter NDVI time-series analysis for the Maroon Basin from 2001 to 2023 showed that the southern and southwestern parts of the basin consistently exhibited the highest vegetation density, while the northern and central areas had the lowest. NDVI values displayed considerable interannual variability, with years such as 2023 recording the highest and years like 2008 and 2012 the lowest vegetation levels. Correlation analysis with ENSO indices revealed that areas with very dense vegetation were most sensitive to the warm phase of ENSO, showing a strong negative correlation with NINO3.4. In contrast, moderate vegetation classes showed a positive correlation with NINO4. Sparse vegetation exhibited weaker responses to ENSO fluctuations. The positive correlation with SOI further indicated that the cold phase of ENSO is generally associated with slight improvements in vegetation conditions. Overall, the findings demonstrate that vegetation in the Maroon Basin is highly responsive to ENSO variability, and the magnitude of this response depends on vegetation type and density. These results are consistent with previous studies conducted in semi-arid regions both within Iran and internationally.ConclusionWinter vegetation in the Maroon watershed was analyzed over a 23-year period (2001–2023) to assess the influence of ENSO indices. The results showed that the southern and southwestern parts of the basin had the highest vegetation density, while the northern and northeastern areas exhibited the lowest density. The strongest negative correlation with ENSO was observed between the NINO3.4 index and the very dense vegetation class (r = -0.68), whereas sparse vegetation classes showed weak responses. The SOI index exhibited a weak positive correlation with dense vegetation. Overall, ENSO had a moderate to weak impact on winter NDVI, while local and ecological factors played a more decisive role in vegetation changes. These findings highlight the importance of long-term monitoring and the concurrent consideration of both local and teleconnection factors for effective natural resource management in semi-arid regions. https://www.sepehr.org/article_731292.html Extended AbstractIntroduction Flooding as a natural hazard usually takes place in many parts of the world and could be a serious threat to the population and environment of the occurring places. So, analyzing the flooding sensitivity is essential for preventing and reducing future hazardous events in each watershed. Therefore, the following objectives are considered in this study: (a) Determining the sensitivity to flooding of sub-watershed based on some morphometric parameters. (b) Calculating the flood peak discharge in each sub-watershed using Rational method. (c) Investigating the relationship between the flooding sub-watershed rank with respect to morphometric parameters and the estimated rank based on the Rational method.Materials & Methods In this study, extracting drainage network and 33 sub-watershed in Kebar-Fordo watershed located in Qom province with an area of ​​128372 hectares were performed by employing Arc Hydro tool in Arc-GIS environment. Then, six different morphometric parameters which affect flood occurrence were calculated. After that, flood sensitivity maps were prepared based on each morphometric parameters while each sub-watershed rank was determined. Finally the total rank of each watershed was estimated by averaging the whole ranks. Due to the lack of adequate observed flood peak discharge values, Rational method was applied to calculate the maximum flood discharge in each sub-watershed. Then Spearman correlation test in SPSS was used to calculate the correlation between the morphometric variable ranks and flood sensitivity of the Rational method.Results & Discussion In this study, the main stream length ranking shows that five sub-watersheds 1, 5, 15, 20, and 28 are more susceptible to flooding. The watershed slope ranking indicate that sub-watersheds 20, 22, 25, 27, 28, 30, 31, 32, and 33 are more sensitive to flooding. Based on the roughness number, nine sub-watersheds have a flood sensitivity ranking of more than 3. The total basin relief parameter, which presents the height difference between the highest point and the outlet of the watershed, determines the runoff potential of a basin. The total roughness in sub-watersheds 31, 32, and 33 is higher than 3, which is evidence of flooding in these sub-watersheds. The mean elevation rank also indicates that watersheds 18, 20, 28, 29, 30, 32, and 33 are prone to flooding with a rank greater than 3. The basin perimeter is one of the effective parameters in runoff production. In this study, sub-watersheds 20, 15, 5, 1, and 28 have flood sensitivity ranks greater than 3. The flood susceptibility map of the studied area based on the average rank of the total morphometric parameters shows that the areas with high, medium, low and very low susceptibility classes include 0.49%, 47.79%, 42.87% and 8.85% of the area, respectively. This map shows that sub-watersheds 32, 31 and 33 are the most susceptible areas to flooding. The rank of slope, roughness number, and total basin relief in sub-watershed 32, is higher than 4, which shows that higher elevations and also greater slope lead to less surface infiltration, more overland flow, and therefore higher peak runoff in this sub-watershed. The calculation of the maximum discharge based on Rational method indicates that the flood ranking which is more than 3, could be seen only in sub-watershed 20 whereas, the values less than 3 could be observed in the rest of the sub-watersheds. Also, the Spearman correlation test shows that the relationship between the flood sensitivity rank of Rational method with the parameters of the perimeter and the stream length is significant at the 99% confidence level and the correlation coefficients are 0.898 and 0.784, while its relationship with the parameters of mean elevation, roughness number, total basin relief and slope is not significant. Also, the correlation coefficient between the flood sensitivity ranks of Rational method and the average flood rank of the morphometric parameters is 0.601 which is significant at the confidence level and indicates a positive relationship between these ranks.Conclusion This research could be conducted by considering the effect of other parameters, such as land use, flood management practices in each drainage basin, and hydraulic structures along the major streams and rivers. The present study demonstrated that morphometric analysis could be used at different scales to help decision makers for understanding the spatial distribution of flood risk and formulating flood control strategies to minimize its negative impacts on residents and infrastructure, and also, proposed a model for continuously updating the flood mitigation plan for the study area. https://www.sepehr.org/article_735947.html Extended Abstract Introduction With the continuous development of society, human disturbance to the ecosystem is also growing, and the ecological environment is gradually deteriorating. This will seriously affect the sustainable development of human society and the ecological environment on which it depends. Generally, ecological vulnerability is the variability of ecosystem under natural or human factors, and this variability is not conducive to the development of ecosystem and human society. Ecological vulnerability is characterized by weak resistance and low resilience of ecosystems in response to external disturbances, including both natural and anthropogenic drivers, within a specific spatial scale. Spatial assessments of ecological vulnerability help identify areas that are exposed to environmental disturbances or pressures, thereby providing a scientific basis for controlling environmental degradation and promoting regional ecological development. In the assessment of ecological vulnerability, multiple variables—such as climate, topography, land resources, and human activities—are influential. The present study aims to conduct a spatial analysis and zoning of ecological vulnerability in the Kaleybarchay watershed. This watershed, located in East Azerbaijan Province, is one of the key regions for nature tourism and ecotourism. Therefore, assessing its ecological vulnerability is essential for sustainable management and conservation. The watershed covers an area of approximately 1,201 km² on the northern slopes of the Qaradagh (Arasbaran) mountain range. Over 27% of the watershed area is covered by dense, semi-dense, or sparse forests. Due to the sensitivity and fragility of this ecosystem, evaluating the ecological vulnerability of the Kaleybarchay watershed is considered necessary. Materials and Methods To achieve the research objectives, the AHP-Fuzzy model and Critic model, the Digital Elevation Model (DEM) of the Kaleybarchay watershed, and Landsat 8 OLI satellite imagery were utilized. The criteria (natural and human) and sub-criteria—including elevation, slope, slope aspect, precipitation, temperature, distance from rivers, lithology, soil erosion, vegetation cover, land use, distance from roads, distance from mines and industries, and distance from residential areas—were determined based on theoretical foundations and previous studies using the Delphi technique. Weighting of the layers was performed using the Analytic Hierarchy Process (AHP) model, while the standardization of the layers was conducted through the Fuzzy logic model. The Critic model was used for validation. After integrating the weighted and standardized layers, a zoning map of ecological vulnerability for the Kaleybarchay watershed was produced. Results and Discussion According to the pairwise comparisons in the questionnaire, the average comparative weights of the criteria and sub-criteria related to ecological vulnerability zoning were obtained. Using the results from the Expert Choice software, weights for each criterion were determined. These weights were then applied to the shape file layers of the criteria in ArcGIS, and through map overlay, the final ecological vulnerability map was generated. The results showed that the criterion “distance from industries and mines” had the highest importance with a weight of 0.216, whereas “slope aspect” had the lowest importance with a weight of 0.009. Land-use change was identified as the second most influential factor affecting ecological vulnerability in the Kaleybarchay watershed. Zoning results indicated that approximately 40% of the watershed area exhibited high to very high ecological vulnerability, mainly concentrated in the central parts of the watershed. Meanwhile, about 40% of the area displayed low to very low vulnerability, predominantly located in the northern and southern regions of the basin. Conclusion The findings of this study indicate that human factors play a more significant role in the ecological vulnerability of the Kaleybarchay watershed. Human activities such as the expansion of industries and mines conflict with the ecological capacity of the region and lead to the degradation of environmental quality. Continued expansion of industrial and mining activities would likely increase the level of ecological vulnerability. Moreover, land-use and land-cover changes are among the major contributing factors. Overlaying the vulnerability map with land-use and vegetation cover layers revealed that areas with high and very high vulnerability mostly overlap with sparsely to moderately dense forests, medium rangelands, and agricultural lands. Among the natural factors, lithological units and soil erosion were identified as the most influential variables affecting ecological vulnerability within the watershed. Comparing the results of the two methods confirmed the accuracy of the zoning. Also, the correlation coefficient between the results of the two methods was 0.89%. https://www.sepehr.org/article_732478.html Extended AbstractIntroductionSmart cities have emerged as an innovative solution for optimal resource management and improving the quality of life. They leverage technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data, and 5G networks to optimize urban services, enhance efficiency, and ensure more equitable resource distribution, thereby reducing spatial inequalities.The main objective of this study was to identify and prioritize the most significant emerging technologies affecting smart city development and to analyze the level of expert consensus regarding their importance and adoption feasibility in urban management.Materials and Methods This research is applied in nature and follows a descriptive-analytical approach. A two-round fuzzy Delphi method was implemented: initially, questionnaires were distributed to 30 experts in urban technologies, and the collected data were analyzed. Subsequently, the coefficient of variation and fuzzy techniques were used to assess and consolidate the level of expert agreement.Findings and DiscussionThe findings indicate that AI and big data achieved the highest consensus and positive impact on improving urban decision-making, optimizing energy consumption, and enhancing service quality. Although IoT has high potential, it faces challenges due to the lack of unified technical standards and insufficient security infrastructure, which placed some IoT-related indicators at the threshold of acceptance. Meanwhile, 5G, as a key communication infrastructure, plays a crucial role in crisis management, intelligent transportation, and remote healthcare services.ConclusionThese results highlight that realizing smart cities requires the standardization of IoT, the development of 5G infrastructure, and strategic investment in emerging technologies. Future research is recommended to adopt a systematic and comparative approach, focusing on technology localization, tailored policy frameworks, and exploring the social and cultural dimensions of technology implementation in diverse urban contexts. https://www.sepehr.org/article_733920.html Extended AbstractIntroduction:Historic urban fabrics represent valuable cultural heritage assets, yet they are often among the most physically vulnerable areas within cities. In seismic regions such as Iran, features including fine-grained urban parcels, narrow and irregular alleyways, aging structures, and traditional low-resistance materials intensify vulnerability, restrict emergency access, and complicate evacuation. The historic fabric of Kashan exemplifies these challenges, making the assessment of safety levels both necessary and urgent.While preserving cultural identity is a key priority, achieving resilience in historic areas requires a careful balance between heritage conservation and disaster risk reduction. The passive defense approach—emphasizing preventive, non-intrusive, and context-compatible strategies—aligns with this objective by enhancing safety without compromising historical authenticity.Previous research on Kashan and similar cities has often focused on regional seismic risk, yet few studies have undertaken a detailed and localized assessment tailored to the specific morphological and structural characteristics of historical fabrics. Moreover, earlier models typically rely on hierarchical approaches that do not consider interdependencies between vulnerability parameters.This study addresses these gaps by applying a multi-criteria framework that incorporates 15 measurable sub-criteria related to accessibility, land-use adjacency, and physical building attributes. By integrating expert-derived ANP weights into GIS and employing Fuzzy Membership functions and spatial overlayering, the research provides a precise, local-scale analysis of physical safety in Kashan’s historical core. The resulting zoning maps serve as a practical tool for planners, heritage managers, and crisis-response authorities seeking to identify priority intervention zones and develop targeted passive-defense strategies.Overall, this research contributes to a comprehensive understanding of physical vulnerability in historic fabrics and underscores the potential of ANP–GIS integration as a robust methodology for enhancing urban safety while protecting cultural heritage. Materials and Methods:This study adopts a descriptive-analytical methodology with an applied purpose. A total of 14 sub-criteria were identified, categorized into three major criteria: (1) physical accessibility, (2) land use and adjacency patterns, and (3) physical characteristics of buildings. These criteria were selected based on their direct influence on the vulnerability of historic urban fabrics.The required data were collected from multiple sources, including base maps, spatial datasets, field observations, and expert surveys. The Analytic Network Process (ANP) was employed to weigh and prioritize the criteria, capturing interdependencies between them. Expert opinions from 15 specialists were used to perform pairwise comparisons, ensuring robust weighting. Subsequently, Geographic Information Systems (GIS) tools were utilized for spatial analysis and mapping. Data layers were standardized, weighted, and integrated through fuzzy overlay and weighted sum techniques in ArcGIS, producing zoning maps that classify safety levels from very high to very low. Results and Discussion:The results show a stark spatial disparity in safety across Kashan’s historic fabric. Out of the total 370.86 hectares:41% of the area falls within low and very low safety zones.Only 12.34% achieves a very high level of safety.The remaining areas are distributed across medium to high safety levels.The most vulnerable neighborhoods include Soltan Mir Ahmad, Darb-e Esfahan, Mohtasham, and especially Taher and Mansour. These areas exhibit high population density, deteriorated buildings, narrow alleys, and reliance on weak traditional materials, all of which heighten vulnerability. In contrast, neighborhoods like Bazaar and Posht-e Mashhad (upper and lower) display higher safety due to partial renovations, better accessibility, and the use of stronger construction materials.The findings highlight that vulnerability is not uniformly distributed; instead, it reflects variations in urban morphology, structural quality, and accessibility. For example, neighborhoods with relatively wider streets and more durable materials, despite being part of the historic fabric, perform better in terms of safety. Conversely, compact areas with aging structures and limited open space show the highest risks. From a passive defense perspective, the results emphasize several strategic needs: 1. Structural reinforcement of historic buildings using context-sensitive retrofitting methods.2. Improvement of street networks to facilitate emergency access and evacuation.3. Expansion of open and safe spaces to serve as emergency gathering points.4. Consolidation of fine-grained parcels to reduce fragmentation and improve resilience.5. Enhancement of social participation, mobilizing local communities in safety planning and resilience initiatives.These findings also underscore the importance of integrating disaster risk reduction with heritage conservation. Without intervention, vulnerable neighborhoods such as Taher and Mansour remain highly exposed to catastrophic risks, representing not only a threat to human lives but also to the continuity of cultural heritage. Conclusion:This research demonstrates that Kashan’s historic fabric, despite its cultural significance, suffers from considerable physical vulnerability that requires immediate attention. Systematic zoning of safety and unsafety levels through GIS and ANP provides a clear framework for identifying priority areas. The study concludes that enhancing resilience in historic urban fabrics necessitates an integrated strategy that balances two key goals: (1) preserving cultural and architectural heritage, and (2) reducing disaster risk through passive defense measures.Ultimately, the approach and methodology applied in this study—combining expert-driven multi-criteria decision-making with GIS-based spatial analysis—offer a replicable model for other historic cities. This model supports policymakers, urban planners, and heritage managers in designing targeted, evidence-based interventions that foster both cultural continuity and urban safety. https://www.sepehr.org/article_732503.html Extended Abstract IntroductionDrought, as one of the most important climate hazards with widespread impacts on environmental sustainability and human livelihoods, requires a multidimensional approach for monitoring and assessment. In this study, with the aim of providing a comprehensive model for assessing drought in northwest Iran, a new composite index was developed based on the integration of four perspectives: meteorology, agriculture, hydrology, and remote sensing. The data used included synoptic observations of the Iran Meteorological Organization, Landsat image time series, and MODIS data for the period 2000 to 2024. All processing and calculations of the indices were performed in the Google Earth Engine environment. The resulting indices were normalized using the Analytic Hierarchy Process (AHP) method and optimized weights, and six-month drought maps were produced. The combined results showed that the severity of drought has increased significantly after 2015, especially in the Lake Urmia basin, while higher mountainous areas show a more stable pattern of moisture. So that the area of ​​severe drought areas in the first half of 2015 reached an area of ​​49.68 km2 and in the second half it reached 34.50 km2, which is almost more than half of the area of ​​the study area. This amount reached 58.70 km2 in the first half of 2019 and 35.20 km2 in the second half, which reached the peak of drought in 2021, so that the first half of this number reached 42.36 km2 and in the second half it reached 41 km2. Also, the combined results of drought maps emphasize that the most droughts occurred in the areas around Lake Urmia and these areas are under serious threat. Finally, the combined method presented in this study provides an efficient method for more accurate spatial and temporal identification of critical areas and provides an effective decision-making tool for managing water resources and agriculture in arid and semi-arid regions.Materials & MethodsThis study introduces a novel hybrid methodology for drought monitoring by integrating four distinct perspectives—meteorological, agricultural, hydrological, and remote sensing—to achieve a more comprehensive and accurate assessment. Focusing on the drought-stricken region of Lake Urmia in the provinces of East and West Azerbaijan, the research combined multi-source data within a Geographic Information System (GIS) environment. To manage the computationally intensive workload, key indices from each perspective were calculated over a 25-year period, segmented into six-month intervals, using the Google Earth Engine platform. The process first synthesized indices within each perspective using the Analytic Hierarchy Process (AHP) algorithm to generate individual drought severity maps. These four distinct maps were then integrated into a single, comprehensive six-monthly drought map through an overlay analysis with equal weighting, effectively transforming qualitative, multi-domain assessments into a quantifiable, spatially explicit drought severity index. The accuracy of this integrated index was validated against meteorological maps derived from reliable in-situ data, offering a refined tool for precise drought monitoring.Results and DiscussionSpatio-temporal analysis of composite drought maps reveals a critical trend of intensifying drought severity in the Lake Urmia basin, particularly from 2000 onwards. The quantitative evidence demonstrates a dramatic expansion of areas classified under "severe dryness," which escalated from approximately 73 km² in the first half of 2015 to 154 km² in the second half, and further soared to 300 km² and 615 km² in the respective halves of 2020. By 2024, the affected area remained persistently high at 310 km² and 200 km² for the first and second halves, marking increases of approximately 24% and 25% compared to the corresponding periods in 2015. Spatially, the results confirm that the most severe drought conditions are concentrated in the lands immediately surrounding Lake Urmia. This spatial pattern suggests a vicious cycle whereby the lake's desiccation exacerbates local agricultural and ecological drought through feedback mechanisms such as salt-dust storms, thereby rendering these areas acutely vulnerable and threatening their long-term habitability if the current trend persists.ConclusionsThis study conclusively demonstrates that the proposed multi-perspective, index-based methodology is a powerful and efficient tool for the comprehensive spatio-temporal monitoring and assessment of drought. Empirical findings confirm a significant intensification of drought within the Lake Urmia basin following 2015, successfully identifying the lake's periphery as the critical epicenter of this environmental crisis. By providing a robust model for monitoring agricultural and ecological drought, this integrated approach equips policymakers and environmental managers with a precise mechanism to pinpoint critical areas at risk. Consequently, the identified regions on the resulting maps offer essential information for planners to implement timely, targeted mitigation and adaptation strategies, thereby enabling proactive measures to combat the devastating effects of drought in vulnerable ecosystems globally. https://www.sepehr.org/article_734053.html Extended AbstractIntroductionPart of the Alpine–Himalayan orogenic belt, shaped by active deformation, folding, and the presence of major strike-slip and thrust fault systems. These processes contribute significantly to the development of surface ruptures, which serve as key geomorphic indicators of crustal stress release and active tectonics. Despite the importance of this region from a neotectonic and seismic-hazard perspective, quantitative and spatially explicit assessments of surface rupture potential remain scarce. Most previous studies have focused either on fault geometry, morphotectonic indices, or seismicity patterns in isolation, while the integrated and probabilistic modelling of rupture susceptibility has received limited attention.Recognizing this gap, the present study develops a spatial–probabilistic framework that integrates multi-source geological, seismological, and geomorphological datasets to predict potential zones of surface rupture within the Kopeh Dagh structural domain. The proposed framework employs a Weighted Linear Combination (WLC) model complemented by Monte Carlo simulation to quantify uncertainty and assess the stability of the model under varying input conditions. By combining these methods, the study aims to provide a robust, reproducible, and spatially coherent evaluation of rupture potential across diverse lithological units and structural environments. Ultimately, this work contributes to a more comprehensive understanding of the tectonic behavior of Kopeh Dagh and enhances regional hazard assessment.Materials and MethodsThe methodological framework is based on a systematic integration of spatial datasets representing seismic, structural, lithological, and geomorphological variables. Earthquake data—including magnitude, depth, and epicentral coordinates—were collected from reliable seismic catalogs and used to model the radius of influence based on empirical magnitude–rupture relationships. Fault density was computed through kernel density estimation, capturing the spatial clustering of active fault traces. Lithological sensitivity was classified according to the mechanical properties of rock units, distinguishing brittle formations from weaker, more deformable sediments. Geomorphological indices such as slope, curvature, and landform type were extracted from high-resolution DEMs to represent surface instability and morphological predisposition to rupture.All datasets were standardized to a common scale and projected into a uniform coordinate system. A Weighted Linear Combination (WLC) model was then applied, incorporating expert-defined weights (0.40 for earthquake influence, 0.30 for fault density, 0.20 for lithology, and 0.10 for geomorphology). This produced an initial rupture-potential index ranging from 0 (very low potential) to 1 (very high potential).To address uncertainty—an inherent component of tectonic and geomorphic processes—Monte Carlo simulation with 1000 iterations was implemented. In each iteration, the weights assigned to input variables were perturbed according to a normal distribution (σ = 0.05), enabling the evaluation of model sensitivity and probabilistic variation. This approach allowed the identification of zones where minor changes in input parameters resulted in significant shifts in potential rupture values, thereby highlighting structurally complex or poorly constrained areas. Model performance and stability were evaluated through the coefficient of variation (CV) and cross-validation metrics, including R² and RMSE.Results and DiscussionThe integrated model demonstrates that tectonic factors overwhelmingly dominate the spatial distribution of surface rupture potential in the Kopeh Dagh region. Among the variables, earthquake magnitude exhibits the strongest correlation with rupture potential (r = 0.85), followed by fault density (r = 0.73). This confirms that areas exposed to higher seismic energy release and greater structural segmentation are inherently more susceptible to rupture propagation. Lithological properties and geomorphological characteristics, while influential, play a secondary reinforcing role rather than acting as primary controls.Spatial analysis reveals that the highest rupture-potential zones are concentrated in the central and western parts of Kopeh Dagh, where active tectonic deformation, dense fault networks, and moderate-to-large seismic events coincide. These areas correspond closely with previously documented neotectonic activity and align with regional patterns of distributed deformation.Monte Carlo simulation results further validate the robustness of the model. The mean potential value across simulations is 0.51, with an average standard deviation of 0.18 and a low coefficient of variation (CV = 0.11). This indicates that the model is relatively insensitive to moderate fluctuations in weighting schemes, and the resulting spatial patterns remain stable across iterations. Areas exhibiting elevated CV values correspond to structurally intricate fault intersections, reflecting known complexities in fault kinematics and stress interactions.The strong agreement between modeled rupture potential and observed seismic–structural patterns is further supported by the high R² value (0.89) obtained during cross-validation. This suggests that the model not only captures the statistical relationships among variables but also succeeds in reproducing the spatial behavior of rupture-prone zones. Overall, the findings underscore the necessity of incorporating probabilistic methods when assessing tectonic hazards in regions where geological heterogeneity and data quality may introduce uncertainty.ConclusionThis study provides a novel and reliable framework for modeling and mapping surface rupture potential in tectonically active regions. The findings highlight the dominant role of tectonic factors, particularly earthquake magnitude and fault density, in determining surface rupture risk. The model’s ability to integrate uncertainty through Monte Carlo simulation enhances its predictive power, making it a valuable tool for future studies in tectonically active regions. The results can inform risk management strategies and contribute to the development of disaster mitigation plans in high-risk areas. It is recommended that future research focus on incorporating higher-resolution data and more accurate field measurements to further improve the model's accuracy and reliability. https://www.sepehr.org/article_728411.html  Extended AbstractIntroductionAccurate and up-to-date data on the structural characteristics of forests, particularly at the individual tree level, are critical for sustainable forest management. Traditional field methods for forest inventory, such as 100% sampling or random sampling, are time-consuming and labor-intensive. Remote sensing, on the other hand, provides a more efficient way to estimate the biophysical and biochemical parameters of plants on a large scale. Satellite imagery has been widely used for this purpose, but it has certain limitations, such as cloud coverage and high costs associated with high-resolution imagery. UAVs (Unmanned Aerial Vehicles), or drones, offer an effective alternative by capturing high-resolution spatial data without the restrictions of cloud interference. They can be equipped with various sensors, such as LiDAR, multispectral, hyperspectral, and infrared cameras, to collect detailed plant data. UAV-based photogrammetry, which processes aerial images to create maps and 3D models, has become a popular method for forest monitoring. Among the commonly used photogrammetry software, Agisoft Metashape and PIX4D are widely adopted for forest science. However, there is limited research comparing the efficiency of these two software programs in estimating critical structural features of forests, such as tree height, canopy area, and density. The primary aim of this study is to compare the accuracy and efficiency of Agisoft Metashape and PIX4D in estimating forest structural characteristics, specifically tree density, canopy area, and tree height, in the Zagros forests of western Iran. These forests are under pressure from over-exploitation, and effective management requires accurate, up-to-date, and cost-effective monitoring methods in these regions.Materials and Methods The study was conducted in the protected forests of Qalaje, located between the Kermanshah and Ilam provinces in Iran. The study area covers 15 hectares, with an elevation of 1,700 meters above sea level, and consists of cold, semi-arid climate condition. The dominant tree species in the study area are Quercus brantii, Pronus microcarpa and Crataegus pontica. A UAV (Phantom 4-RTK) equipped with a real-time kinematic (RTK) system was used to capture high-resolution aerial images over the 15-hectare area. The study area was divided into three groups of sample plots based on canopy coverage density: low (less than 25%), medium (25-50%), and high (more than 50%) canopy coverage. Ten replicates were randomly selected in each category for image processing and field measurement. The flight plan was designed using GS RTK software with a flight altitude of 100 meters. The longitudinal and transversal overlaps were 75%. The images were processed in both Agisoft and PIX4D to create dense point clouds. In Agisoft, a Structure from Motion (SfM) algorithm was used to create dense point clouds, followed by 3D model generation, orthophoto mosaic creation, and extraction of Digital Surface Models (DSM) and Digital Terrain Models (DTM). Similarly, PIX4D followed a comparable process with slight variations in parameter settings. After point cloud generation, the canopy area and tree height were estimated using ArcGIS, where the tree crown area was calculated from the DSM and DTM layers. Field measurements were taken for each plot, including tree density, crown diameter, and tree height. These measurements were used to validate the UAV-based estimates from the two software programs. Paired T-tests were used to compare the estimated tree density, canopy area, and tree height from the UAV images with the field measurements. Linear regression models were also developed to assess the correlation between the UAV-based estimates and field data, with the coefficient of determination (R²) calculated. Additionally, the Root Mean Square Error (RMSE) was used to quantify the estimation error.Results and discussion Regarding the comparison of the performance of the two software programs for generating point clouds, the results showed that the point cloud density produced by Agisoft (1708 points per square meter) was higher than that produced by PIX4D (1498.5 points per square meter). Additionally, the image processing time in Agisoft (242 minutes) was less than that in PIX4D (273 minutes). The results indicated that only in sample plots with low canopy cover density, the estimated number of trees in Agisoft (3.3) and PIX4D (3) did not show a significant difference from the measured number (3.9). In sample plots with medium and high canopy cover density, the error in the estimated number of trees by Agisoft (with values of 55.15% and 70.29%, respectively) was lower than the estimated error by PIX4D (with values of 61.13% and 78.10%, respectively). Regarding canopy cover estimation, the results showed that in Agisoft and PIX4D, the highest RMSE% error in canopy cover estimation was related to sample plots with low canopy cover density, with values of 30.77% and 22.90%, respectively. The results also indicated that with an increase in canopy cover density in the sample plots, the percentage of error in canopy cover estimation decreased relatively similarly in both software programs. Specifically, the canopy cover estimation error for Agisoft at medium and high canopy densities was 27.63% and 8.63%, respectively, while for PIX4D, it was 12.67% and 11.15%, respectively. Regarding tree height estimation, the results showed that the estimated height error at low canopy density was the lowest in both Agisoft and PIX4D, with values of 14.77% and 5.84%, respectively, compared to other canopy density classes. Furthermore, the results indicated that as the canopy density in the sample plots increased from low to medium, the estimated RMSE% error in tree height for the outputs of both Agisoft and PIX4D increased (with values of 23.12% and 12.59%, respectively). However, as the sample plot density increased from medium to high, this error decreased for both Agisoft and PIX4D (with values of 21.34% and 11.69%, respectively).Conclusion In this study, the performance of Agisoft and PIX4D software was compared in estimating the structural features of Zagros forests. Considering the processing time and quality of dense point clouds, it was concluded that Agisoft's performance is superior to that of PIX4D. However, the performance of both software programs for estimating the number of trees, height, and canopy cover is entirely dependent on the level of canopy density. https://www.sepehr.org/article_733439.html Extended AbstractIntroductionWatersheds, as fundamental units of water resources management, possess unique characteristics that influence the occurrence and intensity of floods. In Iran, where the climate is predominantly arid and semi-arid, flood events, particularly in small to medium-sized watersheds like the Torogh Dam watershed, pose significant challenges for water resource management. The Torogh Dam, located in Razavi Khorasan Province near the city of Mashhad, plays a vital role in supplying drinking and agricultural water to the region. Sudden flood events in this watershed can negatively impact the dam's water storage, the safety of downstream areas, and local infrastructure. Therefore, the development of precise and efficient forecasting tools is essential for managing this watershed effectively. Materials and MethodsTo predict floods in the Torogh Dam watershed, Artificial Neural Networks (ANNs) were utilized as a powerful computational tool. This approach involved various stages, including data collection, data processing, and modeling using deep learning algorithms. The methodology for this study is outlined as follows:Selection of the Study Area: The Torogh Dam watershed, located in Razavi Khorasan Province, was selected due to its unique physiographic and hydrological characteristics and its economic significance. With a concentration time of less than three hours, this watershed provides suitable conditions for evaluating the performance of ANN models in flood forecasting.Data CollectionThe data utilized in this study comprised meteorological data (such as precipitation and temperature), hydrological data (streamflow), and physiographic data of the watershed (e.g., area, slope, and river length). Precipitation data were collected daily from reliable meteorological stations and subjected to quality assessments. Statistical methods were applied to correct and fill missing data, minimizing uncertainties in the dataset.Data PreprocessingTo enhance the accuracy of the model, raw data were normalized during preprocessing to ensure all inputs fell within a specified numerical range. Additionally, only precipitation data from one or two days prior to flood events were included in the model to more accurately account for temporal dependencies.Design of the Artificial Neural Network ModelThe ANN used in this study consisted of two primary structures:Two-Layer Network: This structure included an input layer, a single hidden layer, and an output layer. The number of neurons in the hidden layer was determined through trial and error to achieve optimal results.Three-Layer Network: This structure featured two hidden layers and an output layer, designed to improve accuracy and increase the model's regression performance.In both structures, modeling parameters, such as the number of neurons and learning rate, were optimized. The backpropagation algorithm was employed as the learning method.Model Training and EvaluationThe data were divided into two sets: a training set (70% of the data) and a test set (30% of the data). The models were trained using the training set, and their performance was evaluated with the test set. Evaluation metrics included the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).Output AnalysisThe results indicated that using only precipitation data from the day of the flood and the previous day was sufficient due to the short concentration time of the watershed. Increasing the number of neurons in both the two-layer and three-layer networks improved the prediction accuracy, particularly when only precipitation data from the two most recent days were used as inputs to the network. Results and discussionIn two-layer networks, it is observed that when the transfer function of the first layer is Tansig and that of the second layer is Purelin, and precipitation intensity is considered only for the day of the flood and the previous day, the network's output demonstrates a direct relationship with the target and aligns more closely with reality. Increasing the number of neurons under this configuration further improves the results, particularly in both the training and generalization phases.In three-layer networks with a T-T-T transfer function arrangement, increasing the number of neurons enhances regression performance, resulting in outputs that more accurately match reality. This is especially true when precipitation intensity is limited to the day of the flood and the preceding day. In the same three-layer networks, when the number of neurons in the first layer is set to 10 and in the second layer to 15, and the transfer function arrangement is P-T-P, results become more realistic when using only single-day precipitation intensity as input.When the number of layers increases to four, it is observed that if the transfer function for all four layers is Tansig, the outputs of the network matrix and the target exhibit an inverse relationship. Therefore, it is recommended to adopt a configuration in which the last two layers utilize the Purelin transfer function.By transitioning to a cascade-forward network structure, it is observed that a four-layer network with a P-T-P-P transfer function arrangement maintains a consistent direct relationship in most cases except for validation. However, this relationship deteriorates when considering precipitation intensity from the past two days. ConclusionIn conclusion, given the short concentration time of watersheds, accounting only for the precipitation intensity on the day of the flood and the preceding day is sufficient. This has been confirmed in practice in most cases. Additionally, networks perform better when using a Purelin transfer function in the initial layers and a Tansig transfer function in the final layers. Therefore, it is recommended to configure the transfer functions accordingly.For cascade-forward networks, it is preferable to use the Tansig transfer function in the middle layers, while for backpropagation networks, using the Tansig transfer function in the initial layers yields better results. Furthermore, for flood calculations, it is advisable to utilize three-layer or four-layer backpropagation networks, or four-layer cascade-forward networks, as their outputs are closer to the expected real-world values. https://www.sepehr.org/article_716090.html Extended AbstractIntroductionLand use and land cover change is a multifaceted process that is influenced by human activities and natural processes that disrupt the functioning of ecosystems. The rate and intensity of land use and land cover change has increased significantly in recent decades at different levels, from local to global scale. Especially in developing countries, due to unsustainable use of resources and population pressure, the intensity of changes is greater. An important measure to prevent the unwanted and undesirable consequences of the above changes is the systematic evaluation of land use and land cover changes. In this way, the current research has been carried out with the aim of identifying and monitoring land use and land cover changes, and it has been tried to reveal the changes between the years 1999, 2010 and 2023 and predict them until 2043. The results of this prediction help create the necessary groundwork for planning and implementing sound policies regarding the optimal utilization of agricultural and forest lands.Materials & Methods The current research includes a part of the western region of Gilan province, which includes the cities of Bandar Anzali, Soumesara, Foman, Masal and Razvanshahr. In this research, after downloading the Landsat images from the Google Earth Engine platform, random forest algorithm was used for classification and the Quantity and Allocation Disagreement Index were used to evaluate the accuracy of the classified images, and after ensuring the accuracy of the classification, the transformed areas between the classes with the use of GIS was calculated. To improve the accuracy of the prediction model, using the multi-criteria evaluation method, land suitability maps were created based on the physical characteristics of the land and socio-economic factors for each class and integrated with the Cellular Automata-Markov Chain model. The simulated map for 2023 was prepared and compared and validated with the ground reality map of 2023 using Allocation Disagreement, Quantity Disagreement and Figure of merit index. Finally, after confirming the validation results, the pattern of land use classes and land cover for 2043 was predicted by the Cellular Automata-Markov Chain model in the IDRISI TerrSet software platform.Results and discussion The present study examines the changes in land use and land cover in the western region of Gilan province from 1999 to 2023 and estimates the changes until 2043. The basic data in this research are Landsat 5, 7 and 8 images. Image classification and change detection were done using random forest algorithm in Google Earth Engine platform and geographical information system. In order to predict land use changes until 2043, the Cellular Automata-Markov Chain model was used. Then, the accuracy of the classified maps was calculated using Quantity and Allocation Disagreement index. The QADI values for 1999, 2010, and 2023 were 0.009, 0.01, and 0.02, respectively, indicating high accuracy of classification. Also, Kappa coefficient (KC) and overall accuracy (OA) of more than 96% confirm the very good efficiency of RF algorithm in decision making and classification. The multi-criteria evaluation module using ground data was integrated with Cellular Automata-Markov Chain model to increase the accuracy of the prediction model. Validation of the model was done using three criteria: Allocation Disagreement, Quantity Disagreement and Figure of merit. The total value for these three factors was 4.65, 2.02 and 48.60 percent, respectively. The findings indicate a decrease in the area of forest lands, agricultural lands and wetlands, as well as an increase in the built-up areas and range lands in the base period of the study (1999-2023) and the expected decades. This analysis shows the continuation of the trend of reducing the area of natural resources and increasing human activities, spatially urban development in the coming periods. This information can help decision makers in natural resource management to take preventive measures and continuous management to preserve natural environments.Conclusion Predicted maps play an important role in estimating land use changes and natural resources, such as forests, water bodies, biodiversity, soil, minerals and other elements. These maps serve as practical tools for informed decision-making in environmental planning and resource management and enable policy makers to prevent adverse environmental consequences. In this research, three-time Landsat satellite images and random forest classification algorithm were used in Google Earth Engine platform to detect historical changes in land use and land cover from 1999 to 2023. The accuracy of the classified images was verified using the QADI index. In order to improve the efficiency of the of cellular automata-Markov chain model, in predicting future spatial changes, the multi-criteria evaluation method was integrated with this model. Then, by confirming the validity of the simulator model, it was possible to predict land use and land cover changes for 2043. The results showed that, during the study period, built-up areas and range lands have grown by 99.75% and, 6.57 respectively, On the other hand, forest cover, wetlands and agricultural lands lost 3.33, 24.38 and 1.96%, of their area, respectively. The results of the model predicted a significant decrease in the extent of forests, wetlands, and agricultural lands, while increasing the extent of built-up areas. The decrease observed in forest, agriculture and wetland shows the alarming trend of destruction of natural resources and environment.