Geographic Information System (GIS)
Mohammad Karimi; Parastoo Pilehforooshha; Ali Safari
Abstract
Extended Abstract Introduction:The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, ...
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Extended Abstract Introduction:The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, 1994; Carranza et al., 2008a). According to the investigations, the common models of map integration that are used in the discovery of mineral reserves in the initial exploration stage include index overlap model, fuzzy operators, weighted indicators and smart methods such as random forests and artificial networks. Determining the values of weights and scores that show the relative importance of the effective factors is the primary requirement in combining the maps and preparing the mineral potential map (Agterberg, 1992; Brown et al., 2000).The purpose of this research is to prepare a potential map of copper deposits in Dehj-Bazman region using two methods of random forest and support vector machine. In addition, in order to compare the potential map of porphyry copper reserves resulting from the random forest method, the support vector machine method and the knowledge-based methods of index overlap and fuzzy logic were used.Materials & Methods:The area studied in this research is a part of the magmatic belt of Kerman region, known as the Dehj-Sardouye belt. The information layers controlling mineralization in Dehj-Bazman area include rock units, structures, alterations, geochemistry, geophysics and copper deposits. In practical applications of machine learning algorithms, mineral potential mapping is essentially a bimodal classification problem, such that each undiscovered area is classified as prospective or non-prospective according to some combination of mapping criteria (Zuo, 2011). The final results are a set of predictive maps that show target areas with high ore formation potential.In order to model, training was done. Before training the random forest model, the input data set and the target variable should be prepared and then the model should be trained. The target variables for entering the random forest model and support vector machine were determined as deposit points (values of 1) and non-deposit points (values of 0). Then the genetic algorithm was used to adjust the parameters.Evaluation of the predictive performance of random forest model and support vector machine can be described by the ambiguity matrix. In this matrix, there are four components, which are defined as: (1) a deposit sample that is correctly classified as a deposit (TP); (2) a deposit sample incorrectly classified as a non-deposit sample (FN), (3) a non-deposit sample correctly classified as a non-deposit sample (TN), and (4) a non-deposit sample that is wrongly classified as a deposit sample (FP) (Liu et al., 2005; Tien Bui et al., 2016): (8) (9) (10) (11) (12) After training and evaluating different models, the best model was obtained by adjusting different parameters and it was used to integrate factor maps in order to predict areas with high potential of porphyry copper deposits. Also, knowledge-based methods of fuzzy logic and index overlap were used to combine factor maps to compare with the results of intelligent methods.Results & Discussion:At this stage, the desired information layers were collected and prepared in the GIS environment, and then factor maps were prepared. Accuracy, sensitivity, specificity, predicted positive value, predicted negative value, kappa index and OOB error were used to evaluate the performance of random forest model and support vector machine. Also, the importance of the predictor variables in the random forest model was evaluated through the mean decrease in accuracy and the mean decrease in node impurity or the Gini impurity index (Breiman, 2001). According to the results, the most important predictor in the random forest model is the geochemical map, while the structures factor has the least impact in predicting the preparation of the mineral potential map with the final random forest model.In the potential maps of porphyry copper deposits obtained from two methods of random forest and support vector machine, the target areas cover 14% of the studied area, in which there are 92% and 87% of known deposits, respectively. Finally, the efficiency of machine learning methods and knowledge-based methods were compared. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. For this purpose, weights of 0.3, 0.25, 0.25, 0.1, 0.1 were assigned to produce maps of alteration factor, geochemistry, geology, geophysics and structures respectively. In the potential map obtained from the method of index overlap and fuzzy logic (fuzzy sum), the areas predicted as copper mines cover 16 and 17 percent of the studied area, respectively, in which 83 and 79 percent of the existing mines are located.Conclusion:This research was conducted with the aim of evaluating and comparing the effectiveness of random forest method and support vector machine method and knowledge-based methods to prepare porphyry copper potential map of Dehaj-Bozman region of Kerman province. Based on the results, the random forest model works well in the field of porphyry copper potential map preparation with geochemical, geophysical, geological, alteration and structures datasets. In addition, the random forest algorithm can estimate the importance of factor maps.The results of this research show that the geochemical factor map is the most important and the structure factor map is the least important in predicting the data-driven model of random forests. This estimate of importance is consistent with geological knowledge about porphyry copper mineralization in Dehj-Buzman region. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. According to the obtained results, the performance of the random forest model is better than the vector machine model, and also, the performance of the support vector machine model is better than the knowledge-based methods.
Geographic Information System (GIS)
Abolfazl Ghanbari; Mostafa Mousapour; Habil Khorrami hossein hajloo; Hossein Anvari
Abstract
Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of ...
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Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of urban areas is one of the main drivers of global changes that have important direct and indirect effects on environmental conditions and biodiversity. In the process of physical development of the city, due to the transformation of natural and semi-natural ecosystems into impermeable surfaces, it often causes irreversible environmental changes. One of the new approaches in urban planning is the use of remote sensing techniques and geographic information system. The emergence of remote sensing and machine learning techniques offers a new and promising opportunity for accurate and efficient monitoring and analysis of urban issues in order to achieve sustainable development. The process of processing satellite images can generally be divided into two approaches: pixel-based image analysis and object-based image analysis. The pixel-based analysis technique is performed at the level of each pixel of the image and uses only the spectral information available in each pixel. On the other hand, the object-based analysis approach is performed on a homogeneous group of pixels, taking into account the spatial characteristics of the pixels. One of the basic problems in urban remote sensing is the heterogeneity of the urban physical environment. The urban environment usually includes built structures such as buildings and urban transportation networks, several different types of vegetation such as agricultural areas, gardens, as well as barren areas and water bodies. Therefore, in the pixel-based processing approach, the existence of heterogeneity in the urban biophysical environment causes spectral mixing and also spectral similarities in the classification operation of satellite images in such a way that in a place where a pixel is If the surrounding environment is different, it causes Salt and Pepper Noise. Therefore, according to the problems in the pixel-based processing approach, the aim of this research is to compare the accuracy of machine learning algorithms based on object-based processing of satellite images in extracting the physical development area of Hamedan city using Sentinel 2 satellite image.Materials & Methods: The remote sensing data used in this research is a multi-spectral satellite image with a spatial resolution of 10 meters from the Sentinel 2 satellite, including bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared) related to the date is the 23 of August 2023 in the city of Hamadan. The image of the Sentinel 2 satellite was downloaded from the website of the European Space Agency. In ENVI software, the pre-processing operation was performed on the satellite image. Then, in the eCognition software, the segmentation process was performed based on the appropriate scale, shape factor, and compression factor with the aim of producing image objects. After segmenting and converting the image into image objects, using machine learning classifiers based on object-oriented processing of satellite images including Bayes classification algorithms, k-nearest neighbor, support vector machine, decision tree and random trees, the classification process was carried out and maps of urban physical development area were produced. After the segmentation operation and the production of visual objects, three classes of built-up urban land, vegetation and barren land were defined, and some of the built objects in the segmentation stage were selected as training points and some were selected as ground Truth points.Results & DiscussionAfter downloading the satellite image from the website of the European Space Organization, in order to apply the radiometric correction of the image and also with the aim of matching the value of the gray levels of the image with the value of the real pixels of the terrestrial reflection, the gray levels are converted to radiance and then, using atmospheric correction, to coefficients. They became terrestrial reflections. In order to apply radiometric correction, Radiometric Calibration tool was used, and to apply atmospheric correction, FLAASH model was used in ENVI software. In order to classify the satellite image based on machine learning algorithms based on object-based processing, eCognition software was used. The satellite image of the study area, which was pre-processed and saved in TIFF format, was called in the environment of this software and saved as a project. In order to produce visual objects, segmentation operations were performed in different scales, shape factor and compression ratio to reach the most appropriate segmentation mode. In this step, the multiple resolution segmentation method was used to segment the image. The most appropriate segmentation included the scale of 100 and the shape factor of 0.6 and the compression factor of 0.4. Because in scales higher than 100, the construction of the visual object was not done correctly, so that several distinct complications were placed in one piece, and in scales less than 100, in some cases, one complication was placed in several pieces. In order to classify the generated image objects, machine learning algorithms were defined separately and after training each algorithm, the classification operation was performed. In this step, the classification was done based on the nearest neighbor method and by selecting the average and standard deviation parameters for each image band. After producing a map of the city physical development range through machine learning classifiers based on object-based processing of satellite images, the classification accuracy of each of the used algorithms was calculated. In order to calculate the accuracy of the above algorithms in eCognition software, using selected ground Truth control points, the overall accuracy and kappa coefficient were calculated for each of the algorithms.Conclusion:Based on the results of the research, it is possible to produce a map of Hamedan's urban physical development using machine learning algorithms based on object-based processing of satellite images with acceptable accuracy. Also, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and kappa coefficient of 0.96 provided more accuracy.
Geographic Information System (GIS)
Jalal Samia; Manouchehr Ranjbar Shoobi; Amer Nikpour
Abstract
Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among ...
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Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among them, passing through Kandovan road is fascinating with its beautiful natural landscapes. At the same time, this road is also known as one of the most dangerous roads of Iran due to its mountainous location and the potential occurrence of different types of climatic and geomorphologic hazards. Apart from these dangers, the occurrence of accidents in Kandovan road is one of the main concerns of tourists visiting west parts of Mazandaran province and also the local governments providing relief and rescue services and facilities to injured people. Therefore, it is crucial to identifying the dangerous sections of this road in order to minimize fatalities and socio-economic losses. The purpose of this research is to investigate the spatio-temporal density pattern of road accidents and also to identify accidents clusters along Kandovan road.
Material and methods
To this end, we used road accidents information along Kandovan road, collected by the relief and rescue bases of Red Crescent organization of Mazandaran province in the period of 2016 to 2022. Information like location, date, and the number of death and injuries in the road accidents along this road were used in this research. First, we used GIS, spatial and statistical analyses in order to get insight from road accidents distribution and statistics. In the next step, Kernel Density Estimation – a Geostatitical measure – was used to investigate the general spatial density pattern of road accidents in the period of 2016-2022 and also the spatio-temporal density pattern of road accidents in every year from 2016 to 2022. Furthermore, the hot spot analysis was implemented to the distribution of road accidents in this period in order to find out whether accidents are clustered, dispersed or randomly distributed. Both general spatial pattern and annual spatio-temporal patterns of accidents were investigated using hot spot analysis. With this, accidents clusters reflected as hot spots were identified based on the Getis-Ord Gi*index and the associated Z-score, P-value and Gi-bin statistics. In this context, the number of accident clusters, the length of road in the accident clusters and the percentage of observed accidents in the clusters were computed from 2016 to 2022.
Results and discussion
Results show that 2084 accidents were occurred in the period of 2016-2022 with 9076 injuries and 52 deaths. The most number of accidents was occurred in 2022 following the end of Corona lockdown in 2021. Also, several parts of Kandovan road indicated to contain the highest number of accidents density. Besides, the accident density pattern changes spatially and temporarily with an increasing trend in the number of accidents density from the end year of Corona disease epidemic in 2020. Results from hot spot analysis also identified several accidents clusters along this road in the period of 2016-2022. In this context, road accidents clusters were identified in Zangouleh Bridge, Majlar, Siah bisheh, Knadovan tunnel and Ushen Bridge with average Z-score value of 3.12, average P-value smaller than 0.05 and confidence interval of 90 to 99%. The total length of road in these clusters was more than 14 kilometer which contains around 60 % of the total accidents. The spatio-temporal distribution pattern of accidents clusters and also road lengths in the identified clusters change decreasingly in the period of 2016-2022. The results of this research can be used to investigate the reasons behind the occurrence of road accidents in the high accidents density sections and also in accidents clusters identified along the road. Taking proper preparation and mitigation strategies can be beneficial in proper crisis management of road accidents in order to avoid human causalities and socio-economic losses.
Conclusion
We conclude that kernel density estimation and hot spot analysis are effective geostatistical approaches to investigate the density pattern of road accidents and also to identify accidents clusters. In order to increase the safety of Kandovan road, the factors contributing to accidents occurrence in highly dense accidents sections of road and also in accidents clusters need to be identified, and with implementing proper measures, their effects can be minimized.
Geographic Information System (GIS)
Mohammad Reza Pourmohammadi; Reza Karimi
Abstract
Extended AbstractIntroductionThe city, as human life, plays a fundamental role in creating a feeling of satisfaction, and in fact, it shapes the lifestyle of a person and determines the quality of his life. It should be noted that the quality of housing is directly related to the quality of life and ...
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Extended AbstractIntroductionThe city, as human life, plays a fundamental role in creating a feeling of satisfaction, and in fact, it shapes the lifestyle of a person and determines the quality of his life. It should be noted that the quality of housing is directly related to the quality of life and the social and economic development of different communities; in such a way that the accommodation of families in inappropriate housing has become the basis of social problems and anomalies, which in turn, unfavorable social conditions will cause negative economic effects for societies. Quality of life studies can identify problematic areas, causes of people's dissatisfaction, residents' priorities. In life, the influence of socio-demographic factors on the quality of life and to monitor and evaluate the effectiveness of policies and strategies in the field of quality of life. According to the above, the purpose of this research was to measure the quality of life in Urmia city and five regions based on housing indicators. The originality and innovation of the current research can be expressed in the application of the new BWM (2015) and MARCOS (2019) models as well as the combined physical, economic, social and demographic indicators.Materials & MethodsAccording to its purpose, this research is an applied research and according to the method of doing the work, it has a descriptive-analytical nature. The information was collected through library studies, field studies and census data of Iran Statistics Center in 2015. Thus, after studying the sources related to the research topic, 12 indicators have been selected to reach the research output, which include household density in a residential unit, population density in a residential unit, building age, building quality, building strength, land value, access to services. Education and treatment, housing ownership, plot area, infrastructure level, access to green space and residential density. Considering that each of the housing indicators used to model the quality of life has a different importance coefficient, therefore, in this article, the opinions of elites have been used to determine the weight of the indicators. In order to weight the indicators based on the BWM method, 10 questionnaires whose content is based on the pairwise comparison of indicators based on the preference of the best indicator over other indicators and the preference of other indicators over the worst indicator have been compiled. In the next step, the data of the questionnaires were entered into the GAMS software and were calculated and analyzed. The highest weight obtained is related to the building quality index with 0.201 and the lowest is related to the age of the building with an importance coefficient of 0.017. After calculating the weight of the indicators, in order to perform spatial analysis, first the information layers of the indicators were digitized and edited in the software of the geographic information system, and by converting the information layers into a raster and standardizing them based on the purpose of the research, the importance coefficient calculated from BWM in each of The indicators have been multiplied and the combination of the indicators has been discussed by applying weighting. In the last step, the MARCOS model was used to evaluate the quality of life in the five regions of Urmia.Results & DisscussionAfter the 12 indicators of housing to measure the quality of life in the geographic information system software were classified and analyzed using the reclassification command and based on the research objectives, in order to model the quality of life in the city of Urmia by using the summation command and applying the obtained weights Based on the best-worst method, the indicators have been combined. The results of the combination of 12 housing indicators to model the quality of life in Urmia show that 23% of the city is in the very low quality of life zone, 34% in the low quality of life zone, 13% in the medium quality of life zone, 20% in the The quality of life is high and 11% is in the area of very high quality of life. Based on the output obtained from the Marcus model, in Urmia city, region one, region five, region two, region three and region four are ranked 1 to 5 in terms of quality of life based on housing indicators.ConclusionThe obtained results indicate that in general the quality of life in the eastern, northern, and northwestern parts of Urmia city is at an inappropriate level due to the presence of informal settlements, and the Shahrchai River border, which includes the 1st and 5th May areas. Due to the newly built area, it is in high quality. The results of the separate analysis of the indicators show that the quality of life in Urmia city based on the indicators of households in a residential unit, people in a residential unit, the strength of buildings, the area of plots, access to educational and medical services and green space in a medium to high condition and based on the indicators The age of the building, the quality of the building, the value of the land, the level of infrastructure, housing ownership and residential density are in a medium to low condition.
Geographic Information System (GIS)
Omid Faraji; Alireza Gharaghozlou; Hosein Aghamohammadi Zanjirabad; Zahra Azizi; Alireza Vafaeinejad
Abstract
Extended Abstract:Introduction:Undoubtedly, the main motivation of all planning is to achieve sustainable development, regional balance, proper distribution of activities and maximum use of environmental capabilities in the process of regional development. Land is a limited and vulnerable resource, but ...
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Extended Abstract:Introduction:Undoubtedly, the main motivation of all planning is to achieve sustainable development, regional balance, proper distribution of activities and maximum use of environmental capabilities in the process of regional development. Land is a limited and vulnerable resource, but many of its benefits, if not to be abused, are eternal and renewable. In the planning system, the dimension of space is very important and the principles of spatial planning include the principles of sustainability, integrity and comprehensiveness.Developed countries and developing countries both need skills and guidelines in the field of spatial information, methods, frameworks, tools and templates that can use spatial information in timely and accessible decision-making and move forward and to be supportive to sustainable development goals.The purpose of this study is to investigate the applications and political and economic effects as well as the feasibility of using modern technologies such as: Internet of Things - Cloud Computing and Edge Computing - Artificial Intelligence and Machine Learning - Data Mining and Spatial Behavior Mining with the help of Virtual Reality and monitoring Spatial-Temporal changes, all of which are essential to Digital Twin, focusing on land management and sustainable development.Materials & Methods:This article is based on studying the findings and trends implemented in the three leading areas of land administration: The United States, Australia and the European Union and trying to localize those trends in our country.In this research, the comparative study method has been used, which shows a gap of at least ten years between the current situation of spatial information management in our country and what is happening in the leading countries in this field. The study areas include: architecture and structure-political and governance approach-emerging technologies-software- rules and restrictions.The enabler tools used in this research are the complete familiarity with architecture, software, technologies and current instructions in the field of geospatial sciences in the country, and on the other hand, the study of more than eighty articles and more than ten books in the field of the latest global achievements and the review of all reliable portals of the geospatial information and finally a comparative comparison of these two concepts and drawing a road map.Preparation of 2D and 3D cadastral maps of all geographic entities and assignment of legal and ownership information to these entities and then 3D visualization of these combined spatial data in a suitable portal for the preparation of functional spatial analysis are discussed in detail in the implementation method of this research.Results & Discussion:Utilizing the knowledge and technology in creating a Digital Twin platform and adding its powerful tools to the country's national geospatial information infrastructure will lead to maximum productivity and the growth of the economy and social equality, and its highest feedback is the realization of sustainable development in the country.As the Gartner Institute's technology evaluation indexes as well as the CAGR index and the economic impact evaluation reports of the leading countries show, the acquisition of the Digital Twin technology will greatly contribute to the prosperity of the country's economy.In this research, an attempt has been made to mention all the technical and management tools necessary to achieve this infrastructure, and also a prioritization has been done according to the time of achieving the goals.Conclusion:The results of this research show that filling the gap between the current state of the country's spatial information management and the desired state requires investment, culture and serious efforts of those involved in this field, and on the other hand, a positive point can be mentioned that with following the paths taken by developed countries, it is possible to achieve an optimal model and a clear and error-free path to achieve these goals.The localization of global instructions and the development of a conceptual model of action to resolve the existing technology gap will pave the way for the establishment of new technologies in the country, and with the maturity of the technical and operational branches of these models, we will come closer to the realization of sustainable development in the country.On the other hand, without cooperation and coordination between all the governmental bodies involved in the country's geomatic sciences, in addition to the private sector and the society, it seems unlikely to achieve this great achievement, because the creation of such a powerful infrastructure requires a harmonious and coherent national movement.It is hoped that this article will make a small achievement to the formation of strategic thinking in the management of spatial information sciences in the country and provide a complete picture of the dark corners of the development path as well as helping the decision makers in our beloved country.
Geographic Information System (GIS)
Mina Karimi; Mohammad Saedi Mesgari
Abstract
Extended Abstract1. IntroductionIn GIScience, spatial information has usually been presented in the form of space. However, human reasoning, behavior, and perception are mainly based on place, not space. Places are usually ambiguous and context-dependent and are related to the human experience of the ...
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Extended Abstract1. IntroductionIn GIScience, spatial information has usually been presented in the form of space. However, human reasoning, behavior, and perception are mainly based on place, not space. Places are usually ambiguous and context-dependent and are related to the human experience of the world. Place functionality as a context in place descriptions is one of the main and distinguishing features of the place. Today, with the increasing use of users of social networks, volunteered geographic information (VGI) and crowdsourcing information has grown significantly. However, information obtained from social networks, e.g. check-ins, often does not have a complete and clear view of the concept of place and it does not include spatial information between phenomena, land uses, and points of interest (POI). It ultimately limits their ability to work with the concept of place. In this case, GIS should detect the place functionality that does not necessarily exist simply and clearly in the stored data.2. Materials and MethodsTo address these issues, this paper aims to extract place functionality based on analysis of user-generated textual contents. In order to achieve this goal, first places and user’s reviews about places in TripAdvisor website are collected through web crawling. The advantage of these data over other place-based data is their independence from formal descriptions of place. These data were collected in October 2020, and only English reviews are considered. New York City (NYC) is selected as our case study area. At first, for each place type, we extracted all corresponding places. Then, for each place, we extracted a maximum of 1000 top reviews. To prepare data, places without geographic coordinates, places out of the study area, duplicates or places whose type is unknown are removed. There are five types of place categories on TripAdvisor, including Attraction, Food Serving Place, Hotel, Shop, and Vacation Rental. Then, different natural language processing (NLP) methods are used to preprocess the reviews. First, each review is converted to lower case and tokenized, then punctuations and stop words are removed. Afterward, all tokens are stemmed and lemmatized. In the next step, proper features should be selected for knowledge discovery. We use a bag-of-words (BoW) feature selection method which features values are weighted using TF-IDF scores for each user’s review. Finally, in a supervised method, these values and place functionalities are trained using a logistic regression classifier to predict place functionality on the test dataset.3. Results and DiscussionWe randomly assigned 75% of the data set to train the model and 25% to test the results. Finally, the results are evaluated using common machine learning evaluation measures by computing confusion-matrix. The evaluation results demonstrate that the overall accuracy of the proposed method is about 96% which is remarkable. For Food Serving Place, the predictions are so close to reality that in 98% of cases the algorithm was able to correctly predict Food Serving Places. Also, about 0.8% of them are considered as Attractions. In the case of Hotels, the accuracy is 97%. However, about 1.8% of Hotels are incorrectly categorized as Food Serving Places. Attractions are also 93% correctly predicted and about 3.8% of them are mistaken for Food Serving Places. In the case of Shop, the accuracy is about 74%, because the number of reviews related to this type of functionality is lower, although this issue has been partially resolved by weighting the samples. Secondly, in many cases, people visit the shopping malls for entertainment and not just shopping, which has led to about 15% of Shops being classified as Attractions. Also, about 11% of these Shops are considered as Food Serving Places. One of the most important reasons for this is the action of buying food in these places, which is a kind of purchase. In addition, in some shopping malls there are places to serve drink and food. Since the reviews of the Vacation Rentals was less than other functionalities, the lowest accuracy (about 65%) is related to them. In 25% of cases, Vacation Rentals are classified as Hotels. This result is not too far-fetched, as Vacation Rentals and Hotels are very similar in function and are often used to accommodate travelers and tourists. Also, 4.8% and 4.6% of them are classified as Attractions and Food Serving Places, respectively. The maximum precision and F1-score is achieved for Food Serving Places while Vacation Rentals show the least precision and F1-score since their functionality is similar to hotels, however, their results are also reliable and satisfactory.4. conclusionIn this study, we tried to extract the place functionality by analyzing the user-generated textual contents shared on the TripAdvisor website by users. To achieve this purpose, different NLP methods were used to prepare and preprocess the data. The bag-of-words constructed for each user's review was then modeled to a logistic regression classifier, and the place functionality on the test data was predicted. In future works, the efficiency of other feature selection methods as well as other classifiers in extracting place functionality can be evaluated and compared. In addition, the place functionality should be extracted in more detail where different types of attractions can be distinguished.
Geographic Information System (GIS)
Mohammad hassan Yazdani; Ata GhaffariGilande; Farahnaz Veismoradi
Abstract
Extended Abstract1-IntroductionA crisis is a crisis that threatens our country due to special geographical conditions. According to official statistics in the last 29 years, 1% of the country's human casualties were caused by earthquakes, and on average every year an earthquake with a magnitude of 7 ...
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Extended Abstract1-IntroductionA crisis is a crisis that threatens our country due to special geographical conditions. According to official statistics in the last 29 years, 1% of the country's human casualties were caused by earthquakes, and on average every year an earthquake with a magnitude of 7 on the Richter scale occurs in the country for 11 years (Attar), 2012: 8).From the point of view of geology, Iran has a zone of lithological structure such as Central Iran, Zagros, Northeastern Iran, Northwestern Iran and Azerbaijan zone, which the thrust and elevated structure of Zagros is considered as one of the most active of these zones. It includes faults such as Sahne, Durood, Qala Hatem. The seismic characteristics of Zagros show that compared to other structural zones, the frequency of earthquakes is high. The central Iranian and Arabian plates are constantly pressing on the Zagros region, and this is the reason why more earthquakes occur in Zagros than in other regions. The studied area of Kermanshah region is an earthquake zone in terms of seismicity. This province, which is located in the Zagros region, has high seismic activity. These earthquakes usually occur around known seismic faults in the province, including the High Zagros Fault (High Zagros Fault, 2014: 114). Despite numerous researches in the field of locating temporary accommodation on the subject of seismic vulnerability, its suitable and usable place after the earthquake with the approach of crisis management in urban areas has received less attention. The city of Kermanshah is a prone region in terms of earthquakes, and due to its location in a geographical area with a high risk of earthquakes, knowing the vulnerable and resistant areas and planning for the correct and appropriate location of temporary accommodation in time The occurrence of an earthquake is necessary to prevent or reduce the possible danger in this city. This research is complementary to the previous research and with more effective criteria and indicators by providing a practical, efficient, simple and logical method for locating temporary accommodation in order to prepare the city with their specific complexity in responding to crises caused by earthquakes. Is. As an example, the Ezgele earthquake in Kermanshah in 2016 with a magnitude of 3.7 on the Richter scale occurred 11 km from Ezgele section and 32 km from Sarpol-Zahab city, which killed and injured more than 12 thousand people. And since then, due to the lack of single policies in the field of emergency, temporary and permanent housing, problems have been created for housing people. Therefore, the existence of a suitable place to live in Sharat after natural hazards, especially earthquakes, is essential for the city of Kermanshah. The present study, taking into account the stages of crisis management of an earthquake-prone city, tries to address the problem of locating suitable spaces for the construction of temporary accommodation camps.2-MethodologyThis research is of an applied type and according to the investigated components, the approach that governs it is the descriptive-analytical method. The aim of the present research is to analyze the important and influential criteria for the correct location of temporary accommodation in Kermanshah city. In order to achieve this goal, in accordance with the objectives of the research, the required information has been collected using library research, documents and interviews with experts, and then the criteria used for positioning have been selected based on the positioning criteria. The current approach to spatial analysis has been carried out by using the weighted sum model and the ranking method and ArcGIS software.3-Results-Road situation: The analysis of the results of the Kermanshah city situation based on the road access index using GIS analysis software shows that 75% of the city is in the very low vulnerability zone, 7% is in the low vulnerability zone, and 5% is in the vulnerability zone. On average, 3% is in the high vulnerability zone and 10% is in the very high vulnerability zone.-Population density: Examining the results of the state of Kermanshah city based on the population density index using GIS analysis-mapping software, shows that 27% of the city is in the very low vulnerability zone, 27% in the low vulnerability zone, 26% in the medium vulnerability zone. 11% is in the high vulnerability zone and 10% is in the very high vulnerability zone.-Location to administrative, law enforcement and military centers: Examining the results of Kermanshah city status based on the index of access to administrative, law enforcement and military centers using GIS software, shows that 7% of the city area is in the very low vulnerability zone, 12% in the vulnerability zone low, 13% in the medium vulnerability zone, 12% in the high vulnerability zone and 57% in the very high vulnerability zone.-The location of fire stations: the analysis of the results of Kermanshah city based on the index of access to fire stations using GIS analysis software shows that 2 percent of the city is in the very low vulnerability zone, 5 percent in the low vulnerability zone, 8 percent in In the medium vulnerability zone, 32% is in the high vulnerability zone and 53% is in the very high vulnerability zone.-Land slope: This criterion is measured by the slope index. Examining the results of Kermanshah city status based on the land slope index using GIS analytical-mapping software, shows that 46% of the city area is in the very low vulnerability zone, 33% in the low vulnerability zone, 11% in the medium vulnerability zone, 6% in the vulnerability zone. high and 4% is in the zone of very high vulnerability.-Distance from flood-prone areas: The evaluation of the results of Kermanshah city status based on the index of distance from flood-prone areas using GIS analysis-mapping software shows that 16% of the city area is in the very low vulnerability zone, 11% in the low vulnerability zone, 19% in the low vulnerability zone. In the zone of moderate vulnerability, 25% is in the zone of high vulnerability and 29% is in the zone of very high vulnerability.-Distance from hazardous facilities: To measure this criterion, the indicators of electrical facilities, gasoline pumps, gas pumps and gas pressure reduction stations have been used in terms of their functional nature and hazard. Analyzing the results of the state of Kermanshah city based on the index of distance from dangerous facilities using GIS analytical-mapping software, it shows that 13% of the city area is in the very low vulnerability zone, 12% in the low vulnerability zone, 25% in the medium vulnerability zone. 34% is in the zone of high vulnerability and 16% is in the zone of very high vulnerability.-Status of water sources: The results of Kermanshah city situation based on the index of access to water resources using GIS analysis-mapping software show that 9% of the city is in the very low vulnerability zone, 17% in the low vulnerability zone, and 15% in the vulnerability zone. On average, 32% is in the high vulnerability zone and 27% is in the very high vulnerability zone.-Location to power sources Construction of camps and temporary accommodation sites in the power transmission routes due to the provision of lighting and the use of heating devices. The evaluation of the results of the status of Kermanshah city based on the index of access to electricity resources using GIS analytical-mapping software shows that 9% of the city area is in the very low vulnerability zone, 8% in the low vulnerability zone, 8% in the medium vulnerability zone, 26% in The zone of high vulnerability and 49% is placed in the zone of very high vulnerability.-The final map of the optimal location of temporary accommodation bases after defining the important criteria and analyzing the indicators in the studied area is as follows: The results of the combination of 9 indicators in the city of Kermanshah indicate that 1% of the city area In the area of very low desirability, 11% is in the area of low desirability, 37% is in the area of medium desirability, 38% is in the area of high desirability, and 13% is in the area of very high desirability.4-Discussion&ConclusionsIn this research, according to the nature of the research and examination of the environmental conditions of Kermanshah city and the important indicators that are selected and evaluated according to the topic. After examining the determining and influencing factors on the temporary accommodation system and identifying the factors, the results were 9 indicators, which should be applied in the present study with general and specific evaluations (relative to the conditions of the study area). It became a scientific source of consolidation. Investigating and identifying the most accurate features in choosing the location of safe shelters: (slope, access to roads, distance from centers and management, access to firefighting centers, access to water and electricity sources, distance from flood-prone areas, and distance of dangerous risks) is considered And the results of the research showed that the important criteria and options in choosing the right place for temporary accommodation are centers that are identified as the best places that are far from the river and sensitive and dangerous uses such as gas stations and pressure lines. It should be maintained strong and close to essential service centers such as medical centers and fire stations, provided with water and electricity sources, accessible by communication lines, and the probability of damage and blocking of roads should be low. Examining the points selected in the final map shows that in the city of Kermanshah, these points are suitable for use according to important criteria such as: accessibility, distance from sensitive uses, proximity to service areas and distance from flood prone areas. It is in critical condition. The results of the current research show the capability of multi-criteria decision-making methods and geographic information system in identifying areas prone to temporary settlement. Therefore, according to the findings and results of this research, it is suggested that relevant organs, departments and organizations such as the municipality, the governorate, the crisis management center of the Kermanshah Fire Organization, etc., by creating comprehensive and updated databases of all details and elements A city based on the geographic information system should always be prepared for the temporary settlement of the population in the wake of the earthquake crisis. -Preparation of basic and suitable infrastructures for selected sites such as sanitary facilities, water sources, lighting system, etc., so that in case of an earthquake, it has the necessary conditions to accommodate the population, and the need to spend time for not provide these services. Accommodation centers should be located near roads that provide access to different parts on the one hand, and on the other hand, the probability of damage and blockage of these roads is low so that the risk of cutting off access, accommodation, relief and rescue operations does not stop. Zagros seismic zone has its own seismic mechanism. The fault systems and the earthquakes that occurred there follow the special pattern of the Zagros earthquake province, so planning and management should be done by evaluating the existing conditions and taking into account all the conditions of the region and the needs of the people. be carefully examined.
Geographic Information System (GIS)
Hossein Etemadfard; Hamed Kharaghani; Mahdi Najjarian; Rouzbeh Shad
Abstract
Extended AbstractIntroduction:The increasing demand for sustainable food consumption as well as the change in the consumption pattern has led to efforts to improve the food distribution process. This is to speed up service delivery and prevent the spoilage of perishable materials. Among the most significant ...
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Extended AbstractIntroduction:The increasing demand for sustainable food consumption as well as the change in the consumption pattern has led to efforts to improve the food distribution process. This is to speed up service delivery and prevent the spoilage of perishable materials. Among the most significant topics in the food supply chain is perishability, a phenomenon that occurs in certain categories of products such as fruits, vegetables, and dairy products. Perishability refers to the property in which a product loses its commercial value and usability after a certain period. However, meeting the general needs of citizens, especially the supply of food, is one of the most significant axes of urban service activities on the city's economic platform. In addition, the provision of comfort and well-being for residents depends on the proper establishment, optimal distribution, and sufficient variety of products offered in shopping centers. Day markets as well as fruit and vegetable fields provide fast and appropriate daily needs for residents. In addition, choosing fast and reliable routes for food distribution in the city is one of the other significant and influential factors in providing quality services. It should also be noted that in vehicle routing problems (VRP) related to food products, routes for vehicles must be created that match the schedules of some stores to deliver products.Materials and Methods:To optimize the fruit and vegetable distribution routes between the fruit and vegetable fields and Shahre-ma stores in Mashhad, this research will use genetic algorithms and particle swarm algorithms. This research will have the aim of optimizing distribution time, which was not addressed in previous research. This research presents its innovation by considering a three-hour time limit in the problem-solving algorithm. Genetic Algorithm (GA) is a learning method based on biological evolution and influenced by the hypothesized mechanism of natural selection in which the fittest individuals in a generation survive longer and produce a new generation. And in this article, it is implemented in such a way that the algorithm itself determines the most appropriate number of vehicles. The number of vehicles should be such that distribution among all stores is done in less than three hours and five minutes in each store. There should be a stop. And if distribution among all stores is not done in less than 3 hours, a new vehicle will be added to the number of vehicles. Also, particle swarm optimization (PSO) is a technique inspired by the behavior of birds when searching for food. In this research, the data collected include the location of Shahre-ma stores and the fruit and vegetable square in Mashhad city. These data were prepared from the information of Mashhad municipality. Also, to implement these algorithms, MATLAB software has been used. Network analysis has been done to determine the distance between Bar Square and Shahre-ma stores in ArcGIS software using network analysis.Results and discussion:This research proposes several hypotheses, including that the maximum optimal time is 3 hours and products should be distributed by 7 am in all places. Also, city traffic is uniform from 4 to 7 in the morning and the same product package is distributed in all stores. Comparing the results of two genetic algorithms and particle swarm shows that the genetic algorithm has a higher efficiency in optimizing the distribution path of fruits and vegetables. Because the time of the four routes derived from the genetic algorithm is approximately 92 minutes, 84 minutes, 80 minutes, and 82 minutes respectively. The total length of all routes is 127 km and 779 meters and the total time of all routes is 338 minutes. And the time of the four routes obtained from the particle swarm algorithm is approximately 102 minutes, 103 minutes, 89 minutes, and 91 minutes respectively. The total length of all routes is 175 km and 390 meters and the total time of all routes is 385 minutes. And in total, the times obtained for four vehicles in the genetic algorithm were 47 minutes less than the particle swarm algorithm. In addition, the total length of the paths in the genetic algorithm was 47 km and 611 meters less than the particle swarm algorithm.ConclusionThe genetic algorithm was able to achieve the optimal solution by evaluating the objective function 12,000 times. This is 2,900,000 in the particle swarm algorithm. Accordingly, the time required to reach the optimal solution differs significantly between the two algorithms.
Geographic Information System (GIS)
Sakine Koohi; Asghar Azizian
Abstract
Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts ...
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Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). Materials & MethodsASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. Results & DiscussionFindings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. ConclusionResults indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.
Geographic Information System (GIS)
Zhila Yaghoubi; Ali Asghar Alesheikh; Omid Reza Abbasi
Abstract
Extended AbstractIntroductionSelecting a suitable place for a new retail store is a very important decision since new shops cost a lot and new retailers puts themselves at financial risk. Physical location of stores affects the consumer's perception of their first purchase and their subsequent loyalty ...
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Extended AbstractIntroductionSelecting a suitable place for a new retail store is a very important decision since new shops cost a lot and new retailers puts themselves at financial risk. Physical location of stores affects the consumer's perception of their first purchase and their subsequent loyalty to the store. Therefore, spatial analysis is very important for retail stores. Site selection for retail stores has always been difficult and the current competitive market has made decision making even more difficult since stores face increased competition and consumers have many options to satisfy their needs. They generally choose a suitable store in their vicinity which provides high quality, cheap, and diverse products. Therefore, markets and especially retailers shall follow an accurate and valid location strategy for new stores. Retail stores have various marketing and customer service strategies. Marketing strategies require a lot of information about different aspects such as customers, shops, competitors, and products. Many marketing strategies only provide information about consumer behavior or customer satisfaction. However, spatial aspects are more important and in fact determine future success of a store. Several methods are used for spatial analysis in retail sector. The present study use a multiplicative interaction model to forecast sales of confectionaries. This can help retailers develop strategies and find an optimal location for their new stores. Materials & MethodsThe present study has developed a location-based marketing model for online confectioneries in Tehran which can improve site selection strategies of new confectioneries. This marketing model is based on the multiplicative competitive interaction model (MCI) of the retail location theory. To do so, characteristics attracting customers to confectioneries are determined and related data are collected from the Snappfood online platform through web crawling. ArcMap software is then used to analyze and process the collected data. After data normalization, MCI model is implemented using Python programming language. The model is then calibrated using 80% of the collected data and the ordinary least squares (OLS) method. The model is then evaluated using root mean square error (RMSE) method and the remaining data. Results and DiscussionMean errors obtained for districts number 1 to 22 of Tehran municipality show high accuracy of the model. Snappfood site lacked any information about districts number 9 and 18 and thus these districts were not considered in the calculations. Depending on the available data, other districts showed different levels of accuracy. Results indicate that district number 22 had the lowest level of accuracy and district 17 had the highest level of accuracy. In general, this model predicts customer behavior with an error rate of 17.03%. Results of the present study show the probability of purchasing from each confectionery which can be used to map market potential for a new store. This map determines the best place with maximum sale and helps in site selection for new stores based on specific features of the store, competitors and the environment. ConclusionsMCI model predicts sales. From a geomarketing perspective, this model shows that distance between customers and the store and accessibility affect location strategies in new stores. Variables such as pricing and customer satisfaction (scoring) are used to improve the goodness-of- fit of the model. This precise method identifies some key factors to success in a retail strategy. It predicts the probability of purchasing in each district, the number of customers in each store, and distribution of customers in each district. Experts and new retailers can use the results to design various location and sales strategies. Using this model, new retailers in confectionary market can accurately predict their sales before even opening the store and thus protect themselves against possible financial losses. Moreover, this model predicts total sales of different stores and help retailers compare their market shares with those of their competitors. They also can enter features of a new store into the model and find several potential sales strategies. In other words, the model helps determine sales of existing and new shops. In this way, retailers can find an optimum location for their new confectioneries based on the principles of geomarketing.
Geographic Information System (GIS)
Mohamad Amin Daneshfar; Mehdi Ardjmand
Abstract
Extended AbstractIntroductionSuitable sites for waste disposal must leave the least environmental effects while being executable in various aspects. Combination of AHP and GIS is a popular approach used for selecting suitable waste disposal sites, since AHP classifies and prioritizes selected sites ...
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Extended AbstractIntroductionSuitable sites for waste disposal must leave the least environmental effects while being executable in various aspects. Combination of AHP and GIS is a popular approach used for selecting suitable waste disposal sites, since AHP classifies and prioritizes selected sites based on different types of information layers and GIS provides an effective way for data management and display. Various studies have been recently conducted to select suitable sites for waste disposal using GIS and AHP. Rahimi et al. selected a sustainable site for urban solid waste disposal in Mahallat, Iran, using AHP and GIS. Fourteen environmental, economic and social parameters affecting sustainability of landfills were examined in this study and a site was selected in vicinity of this city as the most suitable landfill for solid waste disposal. Improper disposal of waste produced in oil-based drilling of oil and gas wells not only increases costs, but also cause the aforementioned problems. Thus to prevent these problems, it is necessary to select appropriate landfills for this kind of waste.Although, Iranian Offshore Oil Company (IOOC) is responsible for most of oil and gas extraction from Iranian fields in the Persian Gulf, no specific solution has been provided for selection of suitable locations for drilling waste produced in these areas. The present study seeks to select suitable sites for disposal of drilling waste produced in east of the Persian Gulf Iranian oil and gas fields in Lavan Island using AHP method and GIS software. Material and methods Case study Oil and gas fields in Qeshm, Kish, Siri and Lavan operational areas are located in the eastern part of the Persian Gulf. Lavan is one of the islands of Hormozgan province in the Persian Gulf. It is about 2.5 kilometers long and 4.8 kilometers wide. Oil Based Drilling Fluid used in Iranian Oil and Gas Fields in the Persian GulfDue to the type and depth of formation (layers of shale and deep reservoirs), oil based drilling fluids are generally used in Iranian oil and gas fields in the eastern Persian Gulf (Qeshm, Kish, Siri and Lavan). The main component of oil based drilling fluid is petroleum hydrocarbons, especially those with high flashpoint. Usually diesel fuel is used as the main component, which may be added up to 90% to the fluid used in drilling operations. Drilling waste produced in Iranian oil and gas fields in the Persian GulfBased on the latest statistical information provided by IOOC, annually drilling waste is generated by this company in the Persian Gulf, which has declined in recent years due to a reduction in excavation activities. The volume fractions of humidity and oil in the drilling waste are 65% and 30%, which according to the standards of Iranian DOE and HSE unit of IOOC must be reduced to 15% and 1%, respectively after the recycling process. Analytic hierarchy processThe analytic hierarchy process (AHP) is a logical framework that divides complex decisions into hierarchical structures and thus, simplifies their understanding and analysis. This process can be used when decision-making faces some alternatives. GIS Site selection in land-related sciences is an operation through which an expert presents needs, objectives, and information related to the current situation to find the best choice among available alternatives for the concerned land use. The main objective of site selection is to ensure that considering all limitations and available facilities, human activities in the selected site is consistent with the surrounding environment. Nowadays, GIS is used to reach a more scientific and realistic site selection. GIS is a coherent system of hardware, software, data, which allows the storage, analysis, transfer, and recovery of input data and makes it possible to publish the output data as maps, tables, and models of geographical zones. MethodologyThe present study is applied in terms of its objectives and descriptive-analytical in terms of its methodology. The criteria and sub-criteria (layers) involved in site selection for drilling waste disposal in Lavan Island were chosen based on the specifications of the region, recommendations of experts, and related literature. Base data were collected from various sources such as IOOC, Iranian department of environment, and geological survey and mineral exploration of Iran. Accordingly, 15 information layers (sub-criterion) affecting waste disposal site selection in Lavan Island were introduced and classified into three indices. These information layers include industrial building, slope, elevation, gas lines, oil lines, oil storages, roads, population centres, industrial regions, land use, airport, fault line, vegetation, river, and geology which have been classified as technical-economic, social-cultural, and environmental indices (criteria). Figure 1 depicts the hierarchical tree of site selection for disposal of drilling waste produced in eastern Persian Gulf Iranian oil and gas fields in Lavan Island. Figure 1. The hierarchical tree of site selection for oil based drilling waste in Lavan Island Results and discussionProperties of each layer (layer values) were weighted in GIS environment based on al-saati method and experts’ opinions. Classification, weighting and normalization of effective layers used for selecting appropriate sites for oil drilling waste disposal in Lavan Island were performed and results were used to prepare a weighed map for each layer. These maps were combined in the final step to obtain the proposed map for waste disposal site. Figures 2 shows the weights assigned to information layers prepared for waste disposal in lavan Island. Figure 2. Weights assigned to information layers prepared for oil based waste disposal in lavan Island After the internal weighing of each layer, the AHP model was used to prepare the final map for the optimal site. Weighing each of these 15 layers is one of the most important stages of this model in which significance of each layer is expressed compared to other layers. The ultimate normalized weight of each layer was calculated by an AHP matrix with an inconsistency rate lower than 1.0. Chart 1 shows the ultimate normalized weight of each layer which will be used in overlapping operations to find appropriate sites for oil based drilling waste disposal in Lavan Island. Chart 1. Importance of weights assigned to layers in the selection of oil based waste disposal site in Lavan IslandResults indicate that distance from population centers, distance from roads, distance from rivers and distance from airport are the most important parameters used to select appropriate sites for oil based waste disposal in Lavan Island. Results confirm the sensitivity of environmental and socio-cultural criteria for oil based drilling waste disposal in Lavan Island.Then, information layers were integrated using weighted overlay method in AHP to obtain the final map of the appropriate region for waste disposal. In this stage, the layers were overlapped based on their level of effectiveness in GIS environment and the final site selection map was prepared for waste disposal in Lavan Island (see Figure 3). The appropriate sites for waste disposal were classified into 5 classes (from “very good” to “very poor”) and depicted in this map. Figure 3. Classification of selected sites for oil based waste disposal in Lavan Island Spatial analysis of final maps shows that some regions in the center of Lavan Island (sites number 1, 2, 3, 4 and 5)are appropriate for drilling waste disposal due to their distance from population centers, roads, rivers, and the airport. These barren lands are the farthest sites from urban centers, roads, rivers, and the airport. Therefore, construction of waste disposal sites in these regions of Lavan Island is suggested in the final map to decision-makers. Figure 4 shows the prioritized waste disposal sites in Lavan Island. Figure 4. Prioritization of oil based waste disposal sites in Lavan Island ConclusionsThe present study was performed due to the lack of similar studies on waste disposal site selection in this region. GIS and AHP were used to select suitable sites for the disposal of drilling waste in Lavan Island. This drilling waste is produced in the Iranian oil and gas fields in the eastern parts of the Persian Gulf. Effective factors were weighted in different layers of GIS environment and weighted maps were prepared. Priorities were selected using the AHP, and site selection for drilling waste disposal was performed in GIS. Distance from rivers was recognized as the top priority parameter in environmental criteria due to the importance of environmental standards and avoiding surface water pollution. Moreover, distance from population centers, roads, and the airport were selected as top priorities in social-cultural sub-criteria due to the importance of the Island residents’ health and beauty of the landscape. Information layers were thus produced and combined using weighted overlay method in AHP to reach the final maps of suitable locations for oil based waste disposal in GIS. In accordance with effective criteria in the waste disposal site selection, suggested sites were classified into five classes ranging from “very good” to “very poor”. Accordingly, some sites located in the central part of Lavan Island were selected as appropriate sites for the disposal of drilling waste due to their distance from urban and population centers, roads, rivers, the airport, and so forth
Geographic Information System (GIS)
Abolfazl Ghanbari; Vahid Isazadeh
Abstract
Extended AbstractIntroductionAir pollution is a major problemin large industrial cities and affects the life of urban citizens.Due to population growth,significant increase in the number of motor vehicles as well as the concentration and accumulation of industries, Tehran is in the grip of an air pollution ...
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Extended AbstractIntroductionAir pollution is a major problemin large industrial cities and affects the life of urban citizens.Due to population growth,significant increase in the number of motor vehicles as well as the concentration and accumulation of industries, Tehran is in the grip of an air pollution crisis. Previous studies have indicated that once every three days, Tehran faces increased levels of pollutants and air pollution.Ozone is produced through photochemical reactions between hydrocarbons in carexhaust and nitrogen oxides in the atmosphere. Producedthrough reactions between atmospheric pollutants,this pollutant is not primarily released into the environment by a specific sourceand thus, it is called a secondary pollutant.Concentration of ground-level ozone has doubled over the last century.Exposure to this pollutant is very harmful for human health, especially those who exercise outdoors because it severely damages their lungs.Therefore, increased concentration of pollutants has become a major challenge for the management of metropolises such as Tehran. Having information about the spatial distribution of pollutants allows urban managers to take appropriate measures and reduce pollution related risksfor areas and people in danger.Due to excessive concentration of industries and factories inside the geographical boundaries of Tehran, along with its specific geographical condition, topography and climate, Tehran has become one of the seven most polluted cities of the world.The present study seeks to model the spatial and temporal changes of ozone and nitrogen oxidesin Tehran metropolis. Methods and MaterialsIn this cross-sectional descriptive study, spatial analysis of pollutants (ozone(O3) and nitrogen oxides)is performed based on data measured by Tehran air quality monitoring stations for the 2008, 2009, and 2018reference periods. For 2008 reference period, data were collected on a monthly basisfrom the website ofTehranair quality control company,while for 2008 and 2018, data were collected annually. Arc GIS 10.5 released by ESRI was usedfor spatial analysis, and Microsoft Excel 2013 was usedto drawdiagrams and perform other analysis.Inverse distance weighting (IDW) model was used for spatial analysis of ozone and nitrogen oxidesin Tehran metropolitan area inthe three reference periods. Finally, the reference periods were compared and the most polluted one was zoned using the IDW model. In the second method, Google Earth Engine was used to model the spatial distribution of ozone and nitrogen oxides. In this method, Sentinel-5p NRTI O3: Near Real Time Ozone product was used to model ozone and nitrogen oxideson an annual basis (11/01/2018 and 28/03/2020).This is the date in which sentinel has started monitoring ozone and nitrogen pollutants. As the most important product available for measuring the average rate of change,column of ozone and nitrogen oxides’ changes in the atmosphere (O3_Column_number_density) was used in this study. Annual average concentration of ozone and nitrogen pollutants in Tehran was compared with the Sentinel-5 product in Google Earth Engine. Results & DiscussionIn 2018, average annual concentration of ozone and nitrogen oxides in studied stations equaled 12.7 ppb. The accuracy of modeling was also calculated using the coefficient of determination(R2) or coefficient of detection (CD). The average annual concentration of ozone and nitrogen oxides in 2008 was also measured for all air quality control stations to determine their correlation.All independent variables used in this model had an acceptable level of significance (P.> 0.001).In other words, all parameters improved the performance of the model in estimating the concentration of ozone and nitrogen oxidespollutants. The model was developed and R2 rate for 2008 monthly average equaled 0.9188%.The coefficient of determination (R2)for ozone and nitrogen oxides’ concentration in 2009 equaled 0.9134%, but the annual average of 2018showed a much lower R2which equaled 0.476%.It should be noted that not all stations have been evaluated in this study, because the concentrations of ozone and nitrogen oxidesin some air quality monitoring stations equaled zero. Thus, only stations showing a greater than zero value have been used in this study. ConclusionAs previously mentioned, various models have been proposed for modeling the concentration of ozone and nitrogen oxides, each showing a different result. In the present study, the inverse distance weighting (IDW) model was used for three reference periods (2008, 2009 and 2018), and the concentrations of ozone and nitrogen oxides in the atmosphere were also modeled using the variables related to air quality monitoring stations.Ozone concentration modeled by inverse distance weighting method was compared with the average annual change of ozone concentration derived from Sentinel-5 product in Google Earth Engine. Results obtained from the concentration of ozone and nitrogen oxides in the three reference periods were investigated using thecoefficient of detection.The resulting coefficient of determination for ozone concentration in 2008 and 2009 equaled 0.9188% and 0.9134%, respectively. The lowest coefficient ofdetermination for ozone and nitrogen oxidesconcentration was obtained for 2018 which equaled 0.476%. Regarding the spatial distribution of ozone and nitrogen oxides in 2008, the highest concentrations were observed inMasoudiyeh, Punak, Rose Park and Aqdasiyeh stations, and the highest concentration of nitrogen oxides was observed in District4, Crisis Management Headquarterand Sadr Expressway(District 3). In 2009,the station in Rose Park (District 22) showed the highest concentration of ozone and nitrogen oxides.In 2018, IDW modelling and spatial distribution of ozone and nitrogen oxidesshowed a different result. In this reference period, the station in district 4 received the highest annual concentration of ozone and nitrogen oxides, and north eastern areas ofTehran was regarded as the most polluted areas based on the concentration of these pollutants. But stations in16th, 19th and 20th districts and Masoudieh station (15th district) had the lowest annual concentration of ozone and nitrogen oxides. In general, it can be said that spatial modeling with Sentinel-5 product has been able to model the concentration of ozone and nitrogen oxides inall stationsof Tehran on a pixel by pixel basis.
