Moslem Darvishi; Abouzar Ramezani
Abstract
Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption ...
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Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption and overcome this crisis. Due to the rising global temperatures and reportsof the World Wildlife Fund, water scarcitycrisis will dominate most countries of the world, especially in Europe and Asia in the next ten years (Sengupta, 2018). Therefore, advanced water management principles shall be applied to decrease water consumption in the agricultural sector and maintain water security. Iran is among the top five countries of the world in terms of having vast irrigated land (Bruinsma, 2017), which shows that in many parts of the country agricultural lands are irrigated. Thus, the country’s water resources reach a critical stage, and because of limited resources, no more water can be provided for agriculture. The present study primarily seeks to optimize crop cultivation using two approaches: first, reduce water consumption and increase farmers’ income and second, reduce water consumption and meet domestic demand. In order to achieve this goal, first, the type of crops and area under cultivation were determined using remote sensing and satellite imagery. Then,spatial information system was used for data analysisand optimization of crop cultivation. Materials & Methods Remotely sensed images were used to collect data about the area under cultivationin agricultural patches and crop type. Those images were then analyzed using remote sensing techniques.According to pixel-based classification ofmultitemporal satellite images using training data, a croplabel is assigned to each pixelin this method. Moreover, borders of each agricultural land are extracted from pan-chromatic images of the region with higher spatial resolution. Finally, fitting the results of pixel-based classification with the extracted bordersof each agricultural land,a final croplabel is determinedfor the total area of the agricultural landbased on the majority labels. In order to optimize the problem, two objective functions (relationships 1 and 2) are defined in which income maximization and water consumption minimization are considered. Typically, location and allocation problems include objective and constraints functionswhich are maximized or minimized based on the goal of the problem. Linear programming is used to solve the problem. Linear programming is a classical optimization method whichdevelop a deterministic algorithm tosolve the problem. This method can only be used when the relationships between variables are linear. In other words, the relationship between variables shall be perfectly proportional and directin this method. (1) (1) (2) Result &Discussion The study area consists of 198 hectares of agricultural land in vicinity of GolangTapeh village of Asadabad city. The city covers an area of 1195 km2 and constitutes 6.1% of Hamadan province. It is located between 34° 37› to34°50 ‹northern latitude and 47°9› to 47°51›eastern latitude. Its average height is 1607 meters above sea level. The city is bounded in northwest with the province of Kordestan,in west with the province of Kermanshah, in southeast with Tuyserkancity and in the northeast withBaharcity. Assad Abad consists of three plains and a mountainside, but since it mostly consists of fertile plains, it can be considered as a flat area (Fig. 1). Fig1: Case study area Figure 2 shows the results of pixel-basedclassificationusing neural network method. In this method, network is trained using ground data. After training the network on the basis of ground truth estimator data, the estimation accuracy is about 88%. Fig. 2: The results ofclassification using neural network Following the calculation of the area under cultivation in agricultural lands and the type of crops, optimization is investigated using two scenarios (Figure 3). In the first scenario, reduction of water consumption and increased farmers’ income and in the second scenario,meeting domestic demandsto prevent capital outflow is considered. Fig3: Crop type and boundaries of agricultural lands In the first scenario, our priority is to reduce water consumption and increase farmers’ income. In this scenario, the goal is to select the type of crops according to the modeling constraints so that the crop type and water consumption are optimized. Figure 4 shows the proposed crop type. Fig4: The results of thefirst scenario Conclusion The present study used a combination of remote sensing and spatial information system to find a solution for optimization ofcultivation pattern through two different scenarios. First, land boundaries and types of crops were determinedusing pan-chromatic images and artificial intelligence. Then, two objective functions were developed to minimize water consumption and maximize income. Also, constraints such as crop type, periodicity constraints and domestic demand were modeled. Considering two objective functions, an algorithm was presented to optimize the cultivation pattern and the results were implemented in this algorithm. Results indicated that the difference between the first scenario which seeks to minimize water consumption and maximize farmers’ income and the second scenario which seeks tominimize water consumption and maximizedomestically demanded crops is relatively small. In both scenarios, the water use rate inAsadabad plain have decreased by about 1000 m3. In other words, in both scenarios there was a 50% reduction in water consumption. Moreover, if priority is given to meeting domestic demand, water consumption increase by about 3% and income decrease by about 3%. In future studies, owners of each agricultural land can be determined and each farmer’s incomecan be considered to further optimize crop cultivation.
arash zandkarimi; Davood Mokhtari; Shaida Zandkarimi
Abstract
Extended Abstract Introduction The prediction of the occurrence of floods and the reduction of damages caused by it is strongly influenced by the modeling of physical phenomena and the spatial-temporal distribution of precipitation. The purpose of the research was to optimize the rainfall gauging network ...
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Extended Abstract Introduction The prediction of the occurrence of floods and the reduction of damages caused by it is strongly influenced by the modeling of physical phenomena and the spatial-temporal distribution of precipitation. The purpose of the research was to optimize the rainfall gauging network in Kurdistan province using Kriging estimation variance and taking into account the topography of the area. In this study, to optimize the rain gauging network in Kurdistan province, rainfall data of the rain gauging, synoptic, and climatology stations were used. In order to reduce the costs, stations close to each other that are located in the same height range and also have the same error variance, were removed from the existing network. In order to reduce the maintenance cost of the stations, after clustering of the area, 8 stations whose removal had little impact on the accuracy of the data, were identified in the province. Then. In order to strengthen the network, the optimization of new stations was put on the agenda and 28 points were set as the proposed stations. Materials and methods After reviewing the existing stations’ data, 145 stations were selected for the analysis and optimization of the existing network. After selecting the normal data and spatializing them, due to the large extent of the area and the variability of the average annual precipitation, Kurdistan province is divided into smaller regions with less variations in the average rainfall. The regional division or clustering of stations is carried out using the functions available in the ArcGIS 10.2.2 software and based on the main catchment basins. In the next step, the spatial distribution of rainfall and the variance of the errors in all clusters are calculated separately. Given the importance of highlands in receiving rainfall and supplying water, the distribution of rain gauges on elevation layers has been studied. At this stage, redundant stations were eliminated, and stations which are located in close proximity of each other, and are located in the same elevation range and also have the same error variance, can be eliminated too. At the final stage, adding new stations and strengthening the network took place. At this stage, the priority is to build the station for areas where the variance of the errors is high. After adding each station, the error variance of the whole system is calculated again. Adding a new station to the network will continue as long as the network error reaches its minimum. Discussion 1-Normality test of data After spatializing the rainfall data, their normal distribution was investigated using the Kolmogorov – Smirnov test. The results show that the distribution of data at 95% level does not have a significant difference with a normal distribution. 2- Division of the region and clustering of stations In this study, using the region’s digital elevation map, and based on the analyses made in the software ArcGIS 10.2.2, clustering of stations and division of the region was carried out. The entire area of interest is divided into 8 clusters. 3- Calculating the Kriging error of the existing network The amounts of the rainfall data error can be obtained by calculating the Kriging error of the existing network. As mentioned in the previous sections, the calculation of the error in the Kriging method is a function of semi-variogram (spatial structure) of the variable and this feature increases the estimation accuracy of the variable error. 4- Distribution of the stations on elevation layers and determination of the redundant stations By studying the distribution of the stations on altitudes, stations which had no impact on the accuracy of data extraction were removed. The candidate stations for removal were located in a same range of elevation, and showed similar error values. In order to be sure of the decision taken, by eliminating each station, the overall error of the network in each cluster is calculated, and an increase in the error values represents the wrong station is being removed. 5- Adding the proposed stations and calculating the variance of the new network error Adding new stations to the network is done based on the Kriging variance. The priority of the station construction is for areas that display a high error. In the Kriging error variance method, adding a new station to the network is done based on Eó2 (error variance), in a way that points with equal error variance or greater than the value of data variance is considered as the first priority for the construction of the station. The points whose error variances are between the variance of data and ½ of the variance of data, is the second priority and finally, the third priority belongs to the points whose variances are between ½ and ¼ of the variance. In this research, based on Kriging variance, 28 stations have been proposed to strengthen the rain gauging network in Kurdistan Province. Conclusion Given that precipitation is considered as the main entrance to the planning of sustainable water resources development, in this study, the optimization of rain gauging station network in Kurdistan province was investigated using the Kriging error variance. In previous studies, generally, entropy has been considered as the main model for network modification, therefore, due to the limitations of these methods in not using the semi-variogram features, in this research, the geo-statistic method based on kriging error variance was used due to its high accuracy. The amount accuracy increase in this method depends to a large extent on the semi-variogram features (spatial structure) of the precipitation, which can be used to calculate the error variance rate for the new station before the construction and inventory of the station. In order to strengthen the network, the optimization of new stations was put on the agenda and 28 points were set as the location of the proposed stations. For practical comparison of the results, the error variance values were calculated before and after the addition of the proposed stations, the average error variance of the annual precipitation in the province decreased by 11%, with the largest decrease belonging to the central part of the province with 24.03%.
Samira Hosseini; Hamid Ebadi; Yasser Maghsoudi
Abstract
Extended Abstract
Introduction
Estimation of forest biomass has received much attention in recent decades including assessing the capability of different sensor data (e.g., optical, radar, and LiDAR)and the development of advanced techniques such as synthetic aperture radar (SAR),polarimetry and polarimetric ...
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Extended Abstract
Introduction
Estimation of forest biomass has received much attention in recent decades including assessing the capability of different sensor data (e.g., optical, radar, and LiDAR)and the development of advanced techniques such as synthetic aperture radar (SAR),polarimetry and polarimetric SAR interferometry for forest biomass estimation. Accurate estimation of forest biomass is of vital importance to model global carbon cycle. Deforestation and forest degradation will result in the loss of forest biomass and consequently increases the greenhouse gases. Radar systems including SAR have a great potential to quantify biomass and structural diversity because of its penetration capability. These systemsare also independent of weather and external illumination condition and can be designed for different frequencies and resolutions.Moreover, SAR systems operating at lower frequencies such as L- and P-band have shown relatively good sensitivity to forest biomass. Regression analysis is among thecommon methods for evaluation forest biomass which have been investigated for many years on different areas. This analysis is based on the correlation between backscattering coefficient values and the forest biomass. However, previous studies demonstratedthat such approaches are very simple and they do not consider structural effects of different species. One of the restrictions and limitations of these methods is the low saturation level. The level of saturation is lower in higher frequencies and vice versa. Considering the structural parameters, researchers have tried to use the interferometry techniques.Forest canopy height is one of the important parameters that can be used to estimate Above Ground Biomass (AGB) using allometric equations.
Materials &Methods
Recentforest height retrieval methods rely on model based interferometric SAR analysis. The random volume over ground (RVOG) model is one of the most common algorithms. This method considers two layers, one for the ground under the vegetation and one for the volumetric canopy. This model has been investigated in different forest environments (e.g. tropical, temperate and boreal forests). Estimation of forest biomass based on forest height using allometric equations can overcome radar signal saturation to some extent.Improvement of Forest height estimation can play an important role to retrieve accurate forest biomass estimation. In this paper, a new method using scattering matrix optimization is introduced to extract forest height by changing polarization bases. Scattering matrices for slave and master images have been extracted by changing polarization bases. Then polarimetric interferometry coherences have been calculated and forest height was estimated by various forest height methods including DEM Difference, coherence amplitude inversion, RVOG Phase, Combined and RVOG.
Results& Discussion
P-band full Polarimetric synthetic aperture radar (SAR) images acquired by SETHI sensor over Remningstorp (a boreal forest in south of Sweden) were investigated for forest biomass estimation.Mean of Lidar height values which fall in each shapefile was used to check corresponding results with the heights of retrieval methods.
The results of tree height retrieval methods without changing polarization bases between PolInSAR tree height and LIDAR height show that three methods including coherence amplitude inversion, RVOG Phase and RVOG have low R2 value. DEM Difference and combined methods yielded better results in comparison with the other three aforementioned methods; however the results are not satisfactory.DEM Difference method underestimated the tree height compared to that of LIDAR. This is perhaps due to the fact that volume phase center does not lie at the top of the tree.Temporal decorrelation decreases volume correlation, consequently small values in the SINC function lead to generate large values in results; therefore RMSE of coherence amplitude method is relatively high.New master and slave scattering matrices in arbitrary polarization basis were extracted by alteringandin transformation matrix.Results show that RVOG phase has the best result with R2=0.76 and RMSE=3.76. Following this method, DEM difference method shows R2=-0.69.It is likely that methods which include phase information by changing geometricalparameters, in transformation matrix (e.g. RVOG phase and DEM difference) significantly increase the tree height accuracy.sOn the other hand, methods that only apply magnitude of coherence such as coherence amplitude method do not show notable improvementfor retrieving tree height.
Conclusion
Robustness of forest height estimation using Scattering Matrix Optimization by changing Polarization Bases was studied in this paper.PolInSAR data was acquired by SETHI on Remningstorp, a boreal forest in south of Sweden. Results indicated that forest height retrieval methods which included phase parameter shows remarkable improvement by changing the geometrical parameters for height estimation.Therefore RVOG phase method with R2=0.76, RMSE=3.76m and DEM Difference method with R2=-0.69 gave the best results, whereas coherence amplitude method which only included magnitude of coherence with R2=0.17 showed the lowest correlation.
Abolfazl Ranjbar; Farshad Hakimpour; Siamak Talat Ahary
Abstract
Extended Abstract
Introduction
The problem of locating bank branches is classified asNP-Hard problem which can possibly be solved only in exponential time by the increase in the number of banks and the large number of customers, especially when the location model includes various datasets, several ...
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Extended Abstract
Introduction
The problem of locating bank branches is classified asNP-Hard problem which can possibly be solved only in exponential time by the increase in the number of banks and the large number of customers, especially when the location model includes various datasets, several objectives and constraints. As a consequence, we need to use heuristic methods to solve these types of problems. Also, since majority of data and analyses applied in the locating problems are spatial; GIScience’s abilities should be employed besides optimization methods.
Nowadays, to perform particular financial tasks, bank customers often need to be present at their bank. For the sake of its customers, a bank should increase its branches in the city to attract more customers in the race with competing banks. However, establishing new branches is too expensive and banks prefer to carry out an optimal location finding procedure. Such procedures should consider many criteria and objectives including spatial data of customers, new and existing bank branches as well as the level of attraction of banks. Customers often select a bank that is closer to them, has better services or financial records and also consider other human or physical factors. Hence, planning to increase the number of customers for a new branch of a bank considering spatial criteria and various other objectives appears necessary.
Materials & Methods
This paper determines the location of bank branches. Finding an optimum site for branches depends on many factors and these problems are known as NP-hard problems. Despite being approximate methods, meta-heuristic algorithms seem suitable tools for solving NP-hard problems. In this paper, Grey Wolf Optimizer (GWO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Cultural Algorithms (CA) and Invasive Weed Optimization (IWO) are applied for finding the best location for bank branches. From marketing point of view, the aim is to attract more customers while the number of attracted people to a new branch should be acceptable. The new methods have capability to find the optimum location for new branches. The location of a new branch should be as far away as possible from branches of the same bank. The other condition is that the total number of customers for the new branch should not be less than a specified number, while the new branch should not attract customers of old branches of the same bank. To fulfill this propose, a part of the city of Tabriz was selected for implementation.The assumptions for the defined problem can be expressed as the following statements:
a)We consider four different banks (Melli, Mellat, Sepah and Mehr) in our study area.
b)Population density (of people over 15 years of age) is available at the building block level.
c)Banks have infinite capacity for accepting customers.
d)Each customer refers to only one bank.
e)New bank branches should have maximum distance from the branches of the same bank, so that, it attracts minimum number of customers from branches of the same bank.
Conclusion
To evaluate the quality and accuracy of the algorithms, several iterations are performed. The results of statistical and final tests indicate that the accuracy and convergence speed of Invasive Weed Optimization are more than other Algorithms in finding optimal location of bank branches.
Mostafa Kheyrollahi; saeed nadi; Najmeh Neisany Samany
Abstract
Abstract
Due to the sensitivity oftheir missions, urban emergency vehicles are alwayslooking forthe shortest timeto reach the destination. In big cities, in addition todistance, several factors and parameters with respect to the complexityand extent of thetransport and traffic, are influencing time ...
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Abstract
Due to the sensitivity oftheir missions, urban emergency vehicles are alwayslooking forthe shortest timeto reach the destination. In big cities, in addition todistance, several factors and parameters with respect to the complexityand extent of thetransport and traffic, are influencing time of arrival of an emergency vehicle, some of which are qualitative or quantitative, dynamic or static. In this paper, the modern approach used, is based on composing conflation models, Gamma quantification methods, travel time prediction formulas and meta-heuristic algorithms in order to find most optimal route. In this paper, first we have tried to introduce all the calculated, available, qualitative and quantitative, affecting factors related to emergency routing, thenwith converting qualitative parameters to quantitative ones, we normalize each parameter by the maximum approach and conflate them in such a way that thepriority and impact of each parameteris determined to find the optimal route. In order to calculate the priority and impact of factors, the Gamma test method, as a data derived method is selected. The procedure is implemented by the use of road network and traffic volume data from two regions of Tehran. Based on this approach, the considered weights for each following criterion of degree of difficulty including quality, width, slope, category, and route directness are 0.331, 0.286, 0.188, 0.172 and 0.020, respectively. Finally, genetic meta-heuristic algorithm is used to select the optimal route and the results compared with common Dijkstra routing algorithm. The length of the selected route by GA is about 130 meters in one time and about 300 meter in the other time more than the selected one by Dijkstra algorithm. Based on the implemented comparison, the represented approach in this paper had a considerable superiority over the simple current methods.
Ahmad Pourahmad; Hossein Najafi; Roghayyeh Shamsi; Mohammad Fe'li
Volume 21, Issue 83 , November 2012, , Pages 9-15
Abstract
Land and its usage have always been the main theme and context in urban planning and actually it is the land which ultimately determine the destiny of urban development plan, mediating and supervising land use. This has always been one of social, economic and physical issues in recent urbanization. The ...
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Land and its usage have always been the main theme and context in urban planning and actually it is the land which ultimately determine the destiny of urban development plan, mediating and supervising land use. This has always been one of social, economic and physical issues in recent urbanization. The present article considers land use in Sa’d Abad city. First, present situation of the land uses are investigated, then future plans are mentioned. Descriptive-analytic method is used. Theoretical framework is prepared using secondary research and other information are collected in a field study. To perform qualitative analysis, SWOT method is applied. GIS and AUTO CAD are used for drawing and analyzing information. Results indicate that we face shortage of most present land uses and surplus of some others which signifies lack of balance in land use which should be directed towards balance with concise planning.
