aliasghar alesheikh

Accurate crop yield estimation is important for many agricultural issues, including agricultural management, national food policies, and international crop trade. For this purpose, various methods are used to predict product performance, and the use of satellite images increases every day. Satellite remote sensing techniques that cover large areas continuously can help in more accurate assessment of crop yields. This research develops an optimal model for predicting soybean yield in the Midwest region of the United States. The ensemble learning hybrid model was tested using satellite images and meteorological data during the dominant growth period. In particular, the Golden Eagle Optimization (GEO) algorithm was used to adjust the hyper-parameters of the XGBoost model to provide the best possible configuration to improve accuracy. The results showed that the GEO-XGBoost model had good results for soybean crop (R equal to 0.9377 and RMSE equal to 0.2394 tons/ha). These results show that the optimized GEO-XGBoost model can provide accurate predictions for soybean yield under different weather conditions and can also be extended to predict other crops in the future.

Intelligent emergency response systems utilize modern technologies such as the Internet of Things (IoT) to enhance the performance of emergency response units. These systems are designed to improve service quality, reduce costs, and increase monitoring of the emergency response process. Key objectives include optimizing emergency response routes through communication with objects and collecting spatial data. Utilizing IoT-based routing models enables optimizing emergency response routes and enhancing the overall user experience. In essence, these systems leverage data collected by IoT to enhance the emergency response process. Intelligent emergency response systems play a crucial role in improving the efficiency of emergency response units and elevating service levels in emergencies. These systems are readily available and enhance productivity and efficiency in emergencies.

A spatial data infrastructure has been developed to integrate the system and enhance emergency response efforts, providing critical capabilities for improving emergency medical services. This infrastructure includes a portal that accurately displays the optimal route from the incident location to the medical center on a map, assisting the medical team in quickly and efficiently reaching the injured individual. Additionally, this portal enables the transfer of sensor information, such as vital signs of the injured person, to the physician's mobile phone in the ambulance via Bluetooth. This allows the information to be shared simultaneously for further assessment, enabling quick and accurate assistance in emergencies. This system increases efficiency and speed in responding to emergencies, providing rapid and optimal access to medical services. In summary, this spatial data infrastructure has significantly improved the performance of emergency medical response and facilitated the delivery of enhanced and optimized services in emergencies.

Medical centers prioritize healthcare and treatment. They employ an online hierarchical weighting model to ascertain these priorities and enhance the efficiency of resource allocation processes. This model helps optimize resource allocation based on real-time health information of the injured individuals. In a trial case, an injured person was successfully treated in District 5 of Tehran. The efficient use of IoT and spatial data infrastructure enabled this medical center to enhance and optimize its healthcare services. These findings underscore the significance of integrating spatial information, medical data, and IoT technology to advance healthcare services and elevate the quality of treatment.

Traditional emergency response systems operate primarily based on outdated mechanisms and lack modern technologies, including IoT and spatial data integration. Consequently, these systems may encounter challenges such as delays in dispatching emergency personnel to the incident location and a lack of accurate and rapid patient information. Incorporating modern technologies like artificial intelligence, IoT, and geographic information systems can address the challenges faced by traditional emergency response systems. These technologies enable faster and more efficient crisis responses and assist organizations, such as crisis management agencies, in making better resource allocation decisions during emergencies, thereby improving overall performance. By utilizing data collected through these technologies, emergency organizations can significantly enhance their response to emergencies, reducing time, financial, and human costs. Overall, this new approach to emergency response systems enables better adaptability in facing various crises and improves emergency response efficiency.

Dust storms are a severe form of air pollution that poses significant threats to the environment and human health. To deal with this phenomenon, it is crucial to comprehend the mechanisms accountable for dust generation. This can be achieved by utilizing machine learning in dust source susceptibility mapping. Although dust activities vary spatiotemporally due to the constantly changing atmosphere, few papers have addressed dust source susceptibility mapping considering Earth system frameworks such as wet and dry periods. Also, while optimizing hyperparameters is crucial for improving machine learning performance, many studies have neglected this aspect in this particular application. To address this research gap, the objective of this study was to create a framework for mapping the susceptibility of hazardous hotspot dust sources (HDS) during wet and dry periods (based on the changes in water bodies) using a fine-tuned random forest model with teaching learning-based optimization (TLBO) and student psychology based optimization (SPBO) optimizers. To achieve this, the study analyzed 10,392 identified HDS, along with various environmental influential factors between 2000 and 2020 in the transnational shared Tigris-Euphrates Basin, which is a significant source of dust in the Middle East and globally. The results showed that RF-TLBO performed slightly better than RF-SPBO, with an average mean absolute error (MAE) of 0.146, average root mean squared error (RMSE) of 0.194, and average Willmott Index (WI) of 0.761, compared to RF-SPBO's average MAE of 0.148, average RMSE of 0.195, and average WI of 0.757. The TLBO tuned RF with a lower number of trees and a lower maximum depth value, making it a simpler model. We utilized RF-TLBO and observed more areas that are more susceptible to hazardous dust sources during dry periods, confirming the meaningful relationship between wet and dry periods and hazardous dust susceptibility. Higher susceptibilities were found near water bodies and marshlands, indicating the significant impact of fluctuating water bodies on the generation of hazardous dust sources. The Gini index results also show that vegetation cover, elevation, wind speed, and soil texture have a high impact on land susceptibility to be a hazardous dust source.

The shortage and restriction of drinking water and even water consumption in many water-scarce countries of the globe, including Iran, has become an essential and vital issue. This is one of the key reasons that people and communities maximize the development of crops. The best management of crop production, taking into account the requirement for water in the agricultural sector, is one of the acceptable options in nations like Iran, which is also active in the field of agriculture. On the basis of this, it is vital to look at various analytical, planning, and decision-making techniques. Additionally, taking into account and calculating virtual water can significantly improve the cultivation pattern and decrease the amount of water currently consumed; as a result, optimizing the cultivation pattern using virtual water calculations can be highly beneficial. Coherent management and accurate decision-making in a variety of areas will result from the usage and implementation of spatial information systems, and taking into account virtual water will enhance navigation patterns and decrease water consumption. In order to achieve the best allocation of the cultivation pattern and as a result, the correct use of water in the agricultural lands of Ben-Rood district in the operations of Varzaneh city, located in the southeast of Isfahan province, was achieved in the current research by using the decision-making system that was created with the capabilities of GIS and artificial intelligence, along with the virtual water calculations of some agricultural plants. Using the collective intelligence algorithm of the ant community (ACO) in conjunction with the spatial information system, it was possible to achieve this goal after examining the optimization methods in decision-making using environmental parameters. This was done by taking into account the virtual water of cultivation and the growth of plants in the irrigation networks of the agricultural lands of the mentioned sector. Finally, the findings of this study demonstrate that the amount of water consumed may be decreased to 37% of the initial amount after optimizing the allocation of land for the cultivation of specific crops in that area, based on the virtual water of the crops.

Traditional methods of irrigation and resource utilization lack an organized strategy for managing water resources. There are often inefficiencies in the way irrigation water is used in fields and plains, leading to irrigation waste and inefficient water utilization. Considering the size and dispersion of the land, the variety of crops, the time and location at which they are planted, and the distance between the source of water and the point where it is consumed, irrigation and drainage networks are essential for the best management of water resources. Therefore, in the present study, we have attempted to optimize the use of water in the agricultural sector by taking into account both the virtual water of plant cultivation and growth as well as the environmental conditions relevant to it, both geographically and temporally. Based on the calculations presented in this research and comparisons with the existing traditional method, the amount of water consumed in agriculture in the study area, which is part of Isfahan Province's southern part in the Gavkhoni lagoon, is estimated to be approximately 70 percent reduced if the solution presented in this study is implemented. It would be equivalent to using about 30% of the amount of water currently consumed and something equivalent to about 300 thousand cubic meters of water consumption reduction, which is a significant amount.

With the increasing growth of positioning technologies and the use of navigation systems, a large volume of moving point object data, such as people, cars, ships, and animals, is available. However, the lack of integrity and incompleteness of these data for systemic, human, and environmental reasons challenges the analysis of trajectories and their effective application in various fields. Therefore, the reconstruction of missing data plays an important role in maximizing the capacity of movement data, particularly in navigation and track tracking. In this study, using the similarity measurement of trajectories approach, trajectories containing gaps are reconstructed. In this regard, the context-based dynamic time warping (CDTW) method, along with speed and direction movement parameters, are used to measure the similarity and reconstruct the trajectories of vessels in two regions of the Atlantic and Pacific Oceans. Two mechanisms, a constant number of trajectories and a specified threshold, are considered for reconstruction. The results show that using a constant number of trajectories in comparison with the specified threshold reduces the root mean square error (RMSE) and mean absolute error (MAE) from 1.5 and 1.4 to 0.5 and 0.4, respectively. In addition, increasing the length of the trajectories improves the RMSE and MAE values from 0.5 to 0.1 in the case of a constant number of trajectories and 1.5 to 0.3 in the case of the specified threshold.

Spatial equity of urban health (USEH) is closely related to quality-of-life standards and urban development. Its evaluation is very important in the performance of public health and urban planning.  The aim of this research is to develop an approach to prepare and evaluate the USEH map using two data sets including reference location information and Volunteered Geographic Information (VGI).  To determine the accuracy of the maps obtained from both types of data sets. This research was carried out in district 6 of Tehran metropolis. In this research, the effective criteria of the amount of USEH were first determined. The criteria map was prepared with spatial analysis and multi-criteria decision-making, and the final USEH map was prepared by combining the criteria map. To validate the prepared map, the USEH map was again produced based on the VGI information provided by the citizens and compared with the map obtained from the reference data. The amount of USEH for citizens is classified into 5 levels, from very suitable to very unsuitable. Based on the USEH map obtained from the reference data, approximately 62% of the study area has good or very good spatial equity. Comparison of the VGI map with the reference map showed a 72% match. Determining and evaluating USEH for different regions using new methods and technologies is a fundamental step to help health decision-makers in order to manage and allocate resources. It is expected that the results of the present research will be considered an essential criterion for decision-making in the field of health and increasing spatial equity in citizens' enjoyment of health facilities and components.

Nowadays, rapid expansion of urban and inter city industrial areas together industries in adjacent areas are considered as one of the mahn factors affecting increase of environment contamination as well as air pollution.A basic alternatives to reduce and/or control air pollution probem seemed to be proramming a sterategical air pollution management. This study with the aim introducing sterategical air pollution management is conducted in the Isfahan city.The needed data were collected through the filed studies in relation to environmental characteristics affecting rate of air pollution and measured data in the existing air pollution stations.Questionary form were distributed between experts using Dephi method and the gained data were analyzed through the use of combination method of SWOT,QSPM and fuzzy network hierarchical analysis (F.ANP) techniques. Results show that launching the city with emphasis in reducing the use of private cars in combination of policy-making and planning of expanding the public transportation network in the first step and limiting the establishment of industrial units inside the city and gaing polution taxes as well as improving the condition of public roads are in the next step. Accordingly, the most appropriate and practical strategic strategy to control air pollution is reducing the use of private cars with especial emphases on combination of policy-making and planning of expanding the public transportation.

Dust storms are natural disasters that have severely affected human life and the environment. The majority of research in dust storm has been dedicated to the forecasting of storm-prone areas. However, developing models to predict the movement of these storms plays a significant role in the prevention and management of dust storms, because they reveal the transport pathway and identify the next vulnerable areas against the storm. In this research, a hybrid convolutional neural network (CNN) model has been developed to predict the path of dust storms based on airborne optical depth (AOD) data of MERRA-2 product for the next 12 hours. 40 storm events including 2489 storm hours in a dry region in Central and South Asia have been used for training the model. The results show that the proposed model provides an accurate prediction of the storm's path, so that for the time steps of 3, 6, 9, and 12 hours, the overall accuracy values are 0.9806, 0.9810, 0.9813, and 0.9790, respectively, the F1 score values are 0.8490, 0.8524, 0.8530, and 0.8384, respectively, and the Kappa coefficient values are 0.8387, 0.8424, 0.8431, and 0.8273, respectively.

Proper management of epidemic diseases such as Covid-19 is very important because of its effects on the economy, culture and society of nations. By applying various control strategies such as closing schools, restricting night traffic and mass vaccination program, the spread of this disease has been somewhat controlled but not completely stopped. The main goal of this research is to provide a flexible spatio-temporal model for simulating the spread of the Covid-19 disease in order to investigate and evaluate the effectiveness of vaccination. For this purpose, the combination of Agent Based Modelling (ABM) with changeable input parameters and Geospatial Information System (GIS) has been used. The disease spreads through the interaction of the designed agents with each other and with the environment, with the help of the SEIRD epidemic model, and the characteristics of the agents are monitored during the simulation period. To evaluate the model, the real data of patients with the disease in Urmia city from the time of the outbreak to 140 days later were used. The results show that the implemented model simulates the spread of the disease with MAPE= 32.86% and NRMSE= 8.62%. By simulating the vaccination implementation plan, the total number of infected people will decrease by 36.12% and the total number of deaths will decrease by 44.48%. Comparison of simulation outputs and real data shows a similarity of 82% between model results and reality. The result of this research shows that agent based modelling has been able to simulate the spread of the corona virus to an acceptable extent and evaluate the control strategies effectively; Therefore, agent based models can be used to simulate the spread of different variants of the Corona virus and other epidemic diseases, as well as to simulate the environment's response and control strategies.

Rapid and uncontrolled expansion of cities, increased traffic, industrial enterprises and low-quality fuels, as well as urban morphology parameters and climatic conditions are among the factors affecting air pollution in urban areas. In Iran, the metropolis of Isfahan, which is the third largest urban area in the country, has increased air pollution with the extensive development of industrial enterprises, population growth and urban growth. Therefore, in order to find the factors affecting the trend of air quality changes, trend analysis and evaluation of the relationship between land use parameters, industrial development and traffic situation with air pollution indicators were studied.

In order to evaluate the trend using measured periodic data and simple correlation and regression methods of seven air pollutants including PM2.5, PM10, CO, SO2, NO, NO2 and NOX as dependent variables and meteorological parameters, type of land use, industry development and vehicles were analyzed as independent variables. Also in the computational method, SPSS software was used to test the normal distribution of data sets including the concentration of air pollutants and meteorology from 1387 to 1394, in 10 air pollution measuring stations and 3 meteorological stations in Isfahan metropolis.

The results of the study show that the average annual concentration of PM (PM10 / PM2.5), NO and CO decreases and the average annual concentration of SO2, NO2 and NOX increases. In addition, the average annual rainfall, temperature and wind speed increased while the trend of relative humidity in the study area did not change significantly. It was also found that the trend of residential, educational, commercial, public services, transportation and the number of industrial units and vehicles has increased significantly. However, the amount of agricultural land, green space and industrial areas has significantly decreased in the study area. The results of stepwise regression analysis show that changing the use of agricultural land to residential areas and increasing wind speed can be the reasons for the decreasing trend of NO, CO. And suspended particles in the study area. In addition, the increasing trend of transportation can be the most important reason for the increase in NO2 concentration. On the other hand, due to the increasing trend of NOX emissions and significant negative correlation with green space and positive correlation with transportation and industrial areas and the result of stepwise regression model, it can be concluded that reducing green space and an increase of 99.5% in the area of transportation use increases the NOX concentration in the study area. In addition, the size of utility centers increases the concentration of SO2 and there is a negative relationship between the concentration of PM2.5 and SO2 (as the concentration of SO2 increases, the concentration of PM2.5 increases).

It is noteworthy that the relationship between air quality indicators as dependent variables with independent variables in urban areas is complex and it is not clear which specific factor or parameter is the most important scenario of air pollution in an urban context. Therefore, more detailed research is needed.

In recent years, old urban sewage networks have encountered numerous flaws, leading to several important problems in the environment, such as groundwater pollution, excessive growth of tree roots inside sewer pipes, etc. To tackle such problems, innovative approaches must be practiced in urban sewage networks operations. To this aim, a spatial model based on "predictive analysis" and smart technology in the sewage network operation management is needed. Our proposed model was firstly applied for the city of Isfahan to evaluate and predict possible accidents in the urban sewage network. Our model is based on DRASTIC model and Geographic information system. The sewage accidents were assessed by combining the results of DRASTIC model and Getis-Ord Gi* index. This model could assess the previous sewage accidents and predict the probability of future accidents in cities, as well as their environmental risks. In this study, the intention was to identify the hot spots of accidents in the sewage network using GIS; then by studying the factors affecting the accidents, and geological and environmental parameters, a spatial model was designed. Combination of the Getis-Ord Gi* index and DRASTIC model is the main innovation of this research. In the study area, the following items were determined as the most important factors in the sewage accidents: 1- soil type, 2-inappropriate infrastructure, 3-inappropriate pipes with older age, 4-lower diameter. Finally, this model showed that there was a significant relationship between spatial and environmental indices in the study area. Also, the significance value obtained from the statistical analysis of the relationship between pipe diameters and sewer network accidents was equal to 0.004 and the significance value obtained from the statistical analysis of the relationship between pipe life and such events based on Kendall and Spearman tests were calculated as 0.05 and 0.37, respectively.

Every fire accident is accompanied by an uncertainty factor that is caused by human or natural factors. In general, fire hazards are assessed through a variety of predictive models based on the history of large fires. However, researchers and experts point to uncertainty-based fire modeling as one of the challenges to verifying the results. Therefore, the ultimate goal of this paper is to model the fire potential of fire in forest areas by considering the uncertainty due to the weighting of effective spatial criteria based on the Dempster-Shafer Intuition Theory.The innovation of the present study is the application of the Dempster-Shafer Intuition Theory of intuition to reduce the uncertainty caused by the weight of criteria by experts. The necessity of conducting this research, considering the large number of sub-criteria, is managing uncertainty by using the simultaneous use of belief and justification functions, which highlights the possibility of managing uncertainty due to the importance of fire prediction discussion.

First, the effective criteria along with the sub-criteria related to the review of previous research as well as the opinions of experts were identified; Then, a weighting process was performed using the Dempster-Shafer Intuition Theory. To weigh the effective criteria, the opinions of 30 experts were used. Finally, the weighted overlap of the criteria is done and the desired fire hazard map is prepared. The results are evaluated using the ROC curve.Determining effective criteriaTo identify and determine the effective criteria and sub-criteria, the results of previous researches and experts' opinions were used. Finally, 4 criteria include topography, climate, human, and vegetation were considered. Quantitative criteria such as rainfall, temperature, and distance from residential areas that have a direct impact on fire risk were considered for normalization.Criteria and sub-criteria were defined at 3 levels. Then, using the opinion of 30 experts, criteria and sub-criteria were ranked at different levels about each other and based on their importance in fire risk based on the characteristics of oak forest basins located in Islamabad west and Hamil cities. Since there is uncertainty in the opinion of experts, Dempster-Shafer's theory of intuition and the rule were used to integrate opinions, eliminate uncertainty and calculate the importance of each of the sub-criteria and criteria in this study.The theory of intuition and the Dempster-Schaffer ruleThis theory is developed by discussing existing beliefs about a situation or a system of situations. As the belief structure of the control theory is related to the classical probability model. Among the introductory concepts of evidence, the following can be noted: Diagnosis framework, Function to belief and justification, Belief range, Laws of the composition of evidenceResearch scopeThe study area is part of the oak forests of Kermanshah belonging to the west Islamabad and Hamil regions. The climate of this region is temperate Mediterranean and the average annual rainfall is 478 mm. Discussion and findingsTo implement the proposed research method, the maps of the criteria were prepared. Experts were used for the desired intervals of the sub-criteria and were modified if necessary. The criteria and sub-criteria were ranked about each other at different levels. Finally, using the Dempster-Shafer Intuition Theory, the final weights for the criteria and sub-criteria at each level were calculated. The effective parameters of the second level have been calculated using the existing sub-criteria in the third level. The fire hazard for the research area was classified into 4 different classes. Comparison of the resulting map with fires that occurred in the study area shows that high-risk and dangerous classes have a high overlap with points. The ROC method was also used to evaluate the results. 

In the present study, Dempster-Schaefer's theory of intuitive reasoning was used to eliminate the uncertainty in the opinions of experts and to determine the final weight of the criteria and sub-criteria affecting the fire risk. Criteria and sub-criteria effective in causing fire were determined and then classified at different levels. Then, using weighted overlap methods, the criteria according to the final weight obtained from combining the opinions of experts using Dempster-Shafer Intuition Theory, fire hazard map of oak forests in west Islamabad was obtained. Finally, the obtained hazard map was normalized. Then, according to the mean values and standard deviation of the normalized hazard map, the research area was classified into 4 classes in terms of fire risk. The results showed that the most effective indicators in the occurrence of fire are vegetation and then humans, respectively. Comparison of the resulting map with fires that occurred in the research area shows that high-risk and dangerous classes have a high overlap with points. Also, model testing by the ROC curve shows the high accuracy of the model with a value of 92%.

About 80% of world transportation happens at sea. Therefore the safety of vessels, in particularduring vessels’ movement, is crucially important. As different contextual parameters affect vessels’movement, selecting optimal contextual parameters is one of the main changes in vessels’ Context-Aware movement analysis. Toward this end, a Long Short-Term Memory (LSTM) network is usedfor wrapper feature selection to identify optimal contextual parameters for vessels’ movementprediction. To do this, the Automatic Identification System (AIS) dataset from the eastern coast of theUnited States of America collected from December 2017 is used. All possible combinations of threecontextual parameters, including speed, course and vessels’ presence probability in different positionsat sea, were evaluated using the wrapper method in the LSTM network. In all evaluations, 70% ofdata was used for training and the remaining for cross-validation. The results selected speed andpresence probability as optimal contextual parameters for vessel movement prediction. The modeltrained with optimal contextual parameters is 26.98% more accurate than a model trained with allavailable contextual parameters and 16.14% better than a model without contextual parameters.Therefore, selecting optimal parameters from available contextual parameters can help improve theaccuracy of vessels’ predictions. Keywords: Context-Aware, Long Short-Term Memory, AutomaticIdentification System, wrapper, Movement prediction, Context.

Earth's subsidence is one of environmental hazards that humankind faces in recent decades due to the extraction of groundwater resources in the plains. Over the past years, the desertification of plains in Iran has caused a great deal of economic and social damages to the country due to the decline in groundwater levels. Marvdasht Plain Aquifer has been significantly affected by this phenomenon in recent years. On the other hand, considering the location of historical monuments like Persepolis in this village, the issue of subsidence checking is important. Therefore, the present study investigates the relationship between the amount of groundwater extraction and subsidence situation in this region. In this study, Kriging statistical method was used to study the changes in groundwater level, and radar differential interferometry and COSMO-SkyMed radar images were used to estimate the rate of subsidence. Results showed that the average annual groundwater loss is 45 centimeters. On the other hand, the highest rate of subsidence in the oblast area is between 2.5 centimeters and about 15% of the area has a subsidence of less than one cm in the period from 2013/10/01 to 2014/02/01. Comparing the map of the groundwater level and the map of the earth's surface displacement, showed that the areas with the highest groundwater abundance have the highest displacement and ground falls about one cm for every 45 cm of groundwater level drop. Comparison between amplitude and subsidence pattern obtained from the results of radar interferometry technique with location, the density of wells and groundwater abstraction in this plain shows that subsidence has occurred in the same areas where the density of these wells and extraction of groundwater resources is high.

Selecting 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. 

The 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. 

Mean 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. 

MCI 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.

The rail transport system consists of the interaction of a set of equipment and operations that determine the capability and capacity of a rail system in freight and passenger transport. For this purpose, it is important to calculate the capacity and predict how it will change, and knowing it will be of great help in improving the level of operation of the railway network. There are several methods for calculating capacity that can be used depending on the type of network and how it is used. To calculate the capacity, the capabilities of spatial information systems are used and with the help of a web-based spatial information system, the operational capacity of the rail network is determined in a new way and with more efficiency than conventional methods. For this purpose, a GIS-based environment that is connected to various databases of the Railway Company of the Islamic Republic of Iran, including the travel database, is used and while observing the current capacity of the network, through multivariate linear regression, the capacity of the rail network in It determines the future. The present study, through linear regression, predicts railway capacity in a case study in Iran for the three selected routes and identifies important blocks for investigating the effect of spatial parameters in determining the capacity of the railway network. Slowly Based on the available data of 1996 (extracted from the Railway Spatial Web Service), capacity forecasting was performed in 1997 in the GIS environment. The results showed that the capacity utilization of the selected routes for freight trains was 82%, passenger routes 56%, 62% return and 79% combined routes. Also, the accuracy of model prediction for freight trains is 35% better than passenger trains, which is due to the difference in speed change and maximum speed allowed for these two types of trains, and modeling accuracy is directly related to the type of part. Route (passenger, freight and combined), so in the passenger route, the modeling capacity of passenger trains was approximately 45% more accurate. Similarly, on the freight route, the estimation of the capacity of freight trains was associated with approximately 45% higher accuracy than that of passenger trains.

In the last decade, the phenomenon of subsidence has occurred in a large part of the plains of Iran due to excessive harvesting of groundwater and climatic drought. This has caused a great deal of environmental, economic and social damage. In this study, the effect of fine-grained layer thickness and groundwater level drop on the Tehran-Karaj-Shahriar aquifer is investigated. In the first step, fine-layer thickness maps, groundwater level changes, bedrock level, permeability and hydrodynamic coefficients (storage coefficient and transferability) were prepared in GIS. Subsequently, the extent and location of the subsidence map was prepared using differential interferometry and ENVISAT ASAR satellite imagery from 2004 to 2009. The results of this study show a maximum subsidence rate of about 17 cm in the central part of the aquifer and an average groundwater level reduction of 0.42 m / year. Comparison of the subsidence map with the groundwater change map shows that the amount of subsidence in the northwestern aquifer with the greatest decrease in water level (27 m) is low (less than 0.5 cm / year) in contrast to the maximum subsidence (17 Cm/ year) occurred in central areas showing a 5 to 10 m drop in groundwater level. To interpret these results, fine-grained layer thickness maps, permeability, hydrodynamic coefficients and bed scales were used. The results confirm that the areas with maximum subsidence are in accordance with the thickness map of the fine-grained layers.

The heterogeneous urban development and the lack of proper forecasting of urban development cause many problems in cities. Among these problems, there is a shortage of urban amenities such as a hospital, fire station, etc. The 22nd district of Tehran, which has witnessed a growing trend in recent years and includes high-rise towers and buildings, has a large population, so it is believed that the lack of urban amenities, including stations Firefighting. The purpose of this research is to investigate the status of allocation of demand to existing fire stations in the region with the VAOMP unified approach (a completely new approach to location and allocation) with the goal of minimizing time in the GIS environment. Considering that location and allocation issues are among NP-Hard issues, solving them in deterministic methods is almost impossible. For this reason, in the present research, metaheuristic algorithm was used to solve the problem. After sensitivity analysis and implementing the new VAOMP model in the study area with 7 existing stations and a population of 150,000, the results of the research showed that existing stations with the goal of minimizing time (up to 5 minutes) are not able to provide the entire population of the region with services And 28040 calls will remain without access to the services, which means that at least two other stations are required for optimal service provision in the region.. Also, despite the difficulty of the VAOMP model, it was found that this algorithm is capable of solving satisfactorily solvable problems in a short time.

Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations in areas which increase users of this system in different parts of the city. The present study seeks to identify suitable places for the construction ofproposedbus stations in the 6th district of Tehran municipality using GIS functions, Analytic Network Process and Allen’s temporal model.Proposedstationswere then optimized.

Based on necessary investigations about the 6th district of Tehran, 17 indicators were identified: access criterion (sub criteria: business, administrative, medical, religious, educational and sports centers, and urban facilities, subway, roads), demographic criterion (sub criteria:population and employeesdensity) and traffic status (sub criteria: BRT lines, one way and two way streets, street width, traffic load, slop of the area and kind of road).At the first phase, questionnaires were distributed among 35 experts of transportation and traffic. Based on the results of DEMATEL questionnaires and their analysis in MATLAB, the severity of relationship between the criteria were calculated and pairwise comparison questionnaires were designed.Using DEMATEL technique, the presence or absence of a relationship between the aforementioned criteria and sub criteria was investigated. As a decision makingtechnique based on pairwise comparison, DEMATEL uses experts’ judgments to extractelements of a system and find a systematic structure for them using the principles of graph theory. This technique provides a hierarchical structure of the factors of the system along with their corresponding relationship, and determines the effect of these relations in the format of numerical scores.  DEMATEL technique is used to identify and investigate the mutual relationships between criteria and to produce a map of network relations.The ANP model not only calculates the relationship between the criteria, but also the relative weight of each criterion. The result of these calculations make a supermatrix, from which it is possible to derive dependency between each criterion and selection and their weights. An increase in this weight shows higher priority, so it is possible to choose the best option. (Saa’ti, 2003)It is possible to calculate ANP process in both Super Decision and and ANP-solver software. After calculating weight of the criteria, spatial layers are created in GIS software and finally suitable digital layer is created through integration of the criteria. The obtained digital layer shows the best spatial zones for the construction of bus stations in the study area.

Time and place are inseparable parts of each phenomenon in our world. Since, the first step of processing and analyzing a phenomenon in spatial information systemsismodeling, creating a model with necessary capabilities to include temporal dimension is inevitable. One of the main requirements of spatio-temporal modelling is the ability to investigate the topological temporal -spatial relations betweendifferent phenomena. The present study used Allen’s Interval Algebra to extract all relations between different dimensions of time. These include 3 relations between two temporal events, 6 relations between one event and a time mode, and 13 relations between two time modes.Based on Allen’s model, the rush hours were investigated and common temporal – spatial features of each station were obtained. New stations were proposed based on existing stations and the desirable layer, and a desirable time was determined for the buses to pass stations based on land uses around the stations, the rush hours of each land useand common temporal – spatial features of each station (based on Allen’s model).

Results indicate that the ANP and Allen model can only search a very small number of possible answers and reach the required answer. 6thdistrict of Tehran municipality covers an area of 1557.65 hectares, from which 18.10% are in a suitable condition, 21.41% are relatively suitable, 30.45% are moderate, 23.88% are relatively improper and 6.17% are completely improper.281.923 hectares of the district has no problem regarding the access criterion and donot need a station. This district has 185 bus stations and 61 new stations are proposed (a total number of 246).From the aforementioned 246 stations, 17 stations do not have a common schedule, 87 stations have a common point in their schedule, 89 stations have 2, 42 have 3, 10 stations have 4 and one station have 5 common points in their schedule.In terms of time,42.28% stations are in a suitable condition, 36.18% are relatively suitable, 17.07% are moderate, 4.07% are relatively improper and 0.41% are completely improper.Accordingly it is recommended that a bus should pass every 5 minutesfrom stations with 5 and 4 common points in their schedule.For stations with 4 common points in their schedule, this time reaches 10 minutes.Stations with two common points in their schedule need a bus every 15 minutes and stations with 1 common point in their schedule need a bus every 20 minutes.

Numerous routing methods of public transportation systems have been designed. The main challenge of optimum routing of urban public transportation systems is defined as the determination of the optimal locations of the stations and the routh lengths in a city.

To address the challeng designers are confronted with a wide range of criteria, among which cost, time and coathing are the most important ones. In this paper, routing optimization for a configuration of 9 bus lines in one of the Tehran districts was implemented. In order to optimize the bus routes, the origin and destination of each line were considered fixed and the intermediate routes were optimized. Eight factors consisting of: road capacity (LOS), land use, pavement status, crossing line width, components of road cross sections, population density and length of each edge (Arc) were selected for optimization process. According to the importance of each factor for routing, a special weighting coefficient were assigned to each criteria. After implementating Dijkstra algorithm, the routes of 5 bus lines must be changed. The line direction changing subsequently caused to new stations allocation requirement.

After determining the allocation criteria and creating corresponding layers, 16 new stations were allocated. Finally, the proposed routes between these stations were identified.

One of the most important aspects of innovation in this study is the use of GIS capabilities to provide novel solutions for optimal routing of BRT lines.

Location and allocation analysis is one of the most important network and useful analyzes in GIS. This analysis involves various models that, each model is used to solve various problems. Recently, a new model called VAOMP (Vector Assignment Ordered Median Problem) has been developed that, it can solve many different problems. Given that, the location and allocation problems are very difficult, it is almost impossible to solve them in exact methods. Therefore, the present research intends to solve the proper allocation problem of the population to existing fire stations, which are the emergency facilities, and their optimal allocation have a great importance. The VAOMP model and the Genetic and Simulated Annealing algorithms with three goals such as minimizing the arrival time of the fire trucks to the location of demands, minimizing distance and maximizing the coverage of each station in a radius of 5 minutes, solved the problem. The results of this research showed that the Genetic algorithm produces more qualitative solutions in shorter time, while 10 stations in the 21st and 22nd districts of Tehran are not sufficient to serve the total demands in the study area and 55240 people without services will remain and at least, 13 stations in the region should be created.

Landslide is one of the most important types of natural disasters,which endangers lives and financial security of many people and destroys environment and natural resources.With the present population growth and expansion of urban areas towardsteep areas and hillsides, landslide-related losses can be catastrophic. For an instance, landslides in Badakhshan Province in Afghanistan killed around 2,700 people in 2014, and a landslide in China (Shan’xiprovince)resulted in the disappearance of 64 people in 2015.Therefore, assessingthe possibility of landslides occurrence seems to becrucial. Providing zoning maps is one of the measures which makes identification of areas prone to future landslides possible. Inferences drawn from these maps can be used for land use planning, prevention of unauthorized construction activities, infrastructure development, refurbishment and restoration.

The present research selectsEast Rudbar-e Alamut (a district of Qazvin province), which is affected by landslides and instability of hillsides, as the study area. It takes advantage of Shannon entropy and information value models to develop landslide susceptibility map of the study areain GIS environment.Shannon entropy theory has been used in extensive researcheswith the aim of prioritizinginfluential factors in the probable occurrence of natural disasters such as landslide. Information value (IV) model is one of the statistical models drawn from information theory with a widespread application in the modeling of geological hazards and disaster risk assessment. Information value model aims to find a combination of significant factors anddeterminetheir impacton theoccurrence oflandslide in an area.To implement this model, relevant data and its related criteria maps were prepared. In this regard, the location of previous landslide events in the study area was determined based on the information received from Forests, Range and Watershed Management Organization. 49 landslides were identified in this way. Then, data was randomly divided into 2 categories: training data and validation data. Thus, 70% of data (35 landslides) were used to produce the models and the remaining 30% (14 landslides) were used for validation purposes. In addition to previous landslides, a collection of topographic, environmental and climatic characteristics of the study area including seven criteria of lithology, slope, distance from faults, land use, precipitation, slope-direction and elevation were selected as the most effective independent variablesto produce criteria maps with 30-meter spatial resolution. Basic information used to map these seven influential factors was obtained from Forests, Range and Watershed Management Organization, as well as the SRTM Digital Elevation Model (DEM), and used after some modifications. Considering the capability of ArcGIS in spatial data analysis, thissoftwarewas used to produce information layers and implement the models.

Prioritizing influential factors using Shannon entropy model introducesthree factors (i.e. land use, elevation and precipitation)as the most significant factorsin the occurrence of landslides in the study area. Factors of slope angle, distance from faults (almost equal to slope angle), lithology and slope-direction were in the next influential factors.Also, results of information value model indicate that looking from lithology perspective, the category of marl, calcareous sandstone, sandy limestone and minor conglomerate has an information value of 1 and thus, the highest probability of landslide occurrence. Category of basaltic volcanic rocks, along with category of well bedded green tuff and tuffaceous shale have the lowest probability of landslide occurrence with information values ​​of -2.03 and -1.70, respectively.Only two categories of theslope angle criterionhave a positive-index. The highest information value (0. 93) in this category occurs in the class of 5-12 degrees, followed by the class of 12-20 degrees. The lowest information value occurs in slopes of more than 30 degrees. Based on this observation, it can be clearly concluded that the slope angles of 5 to 20 degrees are most prone to landslides. Distance to faults criterion indicate that the category of500 to 1000-meter distance to faultshave the highest information value (1.67). Regarding land use criterion, three land uses of garden, agriculture and garden-agriculture have the highest information values ​of 2.16 and 1.59 and 1.11, respectively. Regarding precipitation, average annual rainfall of less than 400 millimeters have the highest information value (1.50). Regardingslope-direction criterion, most landslides occur in southwest, south and eastdirections.Northeast, west, and northwest directions have the lowest probability of landslide occurrence, respectively. In terms of elevation, the information value is reduced as the height increases, and the maximum information value is related to the elevations of less than 1200 meters.After assigning a weight to each criterion and related classes, the landslide risk zone map was generated based on Shannon entropy and information valuemodels. The resulting zoning map produced based on natural breaks methods dividesthe area into five classeswith very high, high, moderate, low and very low risk. Resultsof Shannon entropy modelindicate that out of 14 landslides considered as the validation data, 3, 7, 2, 1, 1 landslideshave occurred in very high, high, moderate, low and very low risk zones, respectively. Resultsof the information value modelindicatethat 8, 4, 0, 1, 1 landslideshave occurred in very high, high, moderate, low and very low risk zones, respectively.

Evaluation of results using experimental probability index indicates that with 86% experimental probability,both models of Shannon entropy and information value are effective inidentification of landslide hazard in the East Rudbar-e Alamut region. Also, considering the number of landslides in very high and high risk zones, Shannon entropy and information value modelshave an experimental probability index of 72% and 86%, respectively, which prove higher efficiency of information value model. In Shannon entropy model, total area of very high, high and moderate risk zones covers 34% and 56% of the study area,respectively. In information value model,total area of very high and high risk zones covers 20% and 29% of the study area, respectively. Based on the landslide risk zone map, high and very high risk zones are mainly located in the west of the study area

Oak is a common species in Iran and the most important one in Zagros forests. Zagros forests play a crucial and effective role in water supply, soil conservation and climate modification in Iran. Unfortunately, a significant part of those forests suffer from oak decline. Oak decline (or oak mortality) is a widespread phenomenon in oak forests around the world, which has gained the attention of many researchers in forestry over the past decade. In Iran, this phenomenon was first observed in Zagros forests in 2013. Factors affecting oak decline and their mutual interactions are not clearly identified, which makes understanding and modeling of these processes challenging. Only a few studies have been performed in relation to this phenomenon in Iran. Thus, we chose to determine the most effective parameters and find the best modeling method for oak decline in Iran and especially in Lorestan province.

In order to find effective environmental variables, related literature review was thoroughly investigated. Environmental parameters including temperature, precipitation, elevation, slope, direction, soil type, and amount of aerosols were selected as basic influencing parameters. All parameters were then interpolated to produce raster data with 30-meter cell resolution. To find the optimal combination of the parameters, four operators including multiplication, logarithm, hyperbolic transformations, and principal component analysis (PCA) were used. A total 385 different combinations of the influencing parameters were produced using the above mentioned operators. The relation and weight of each parameter are unknown, thus Artificial Neural Networks were used to model oak decline process. Three feed forward artificial neural network, including Back-propagation Neural Network (BP), Probabilistic neural network (PNN) and Support Vector Neural Network (SVNN) were selected as modeling methods. Then, 385 different combinations of the influencing parameters were used in the above mentioned models. To train and evaluate each neural network, a total number of 10000 samples were randomly selected from the study area. 70 percent of these random samples were used to train, 15 percent to evaluate and 15 percent to validate the models. Also, cross-validation method was used to avoid over fitting of neural networks. Finally, 1155 created NN models were compared using R parameter to find the best configuration for modeling oak decline and identifying the most influential environmental parameters in oak decline.

Evaluating 1155 different networks indicated that Probabilistic neural network (R=0.87) with 6 inputs, including 1) elevation, 2) slope, 3) direction, 4) aerosols, 5) soil type and 6) principal component of temperature and precipitation, performed better than SVNN and BP in modeling oak decline. Moreover, using different combinations of influencing factors improved the results and increased correlation coefficient (R) of optimal inputs by 0.05 as compared to initial inputs. Thus, it can be concluded that increased number of inputs does not necessarily guarantee a better performance. Furthermore, two principle parameters of temperature and perception have a more significant role in modelling drought stress as compared to other parameters.

Oak decline is a complicated phenomenon and different factors contribute to its occurrence. The present study investigates all environmental parameters affecting oak decline through a comprehensive literature review. Results indicate appropriate performance of probabilistic neural networks in modeling oak decline. Moreover, principal component analysis is considered to be a useful tool for modeling of drought stress in oak trees. Due to different accuracy and precision of these neural networks, it is necessary to evaluate different configurations. For further researches, it is suggested to use other parameters, such as distance from population centers, water table, age of oak trees, oak tree height and characteristics of other nearby trees.

Iran is a vast country that has long been an area of the Earth’s crust caused by the release of energy accumulated in it, always large and destructive shake occurs. Therefore, the analysis of natural hazards is essential because it can make decisions and prioritization of actions in crisis management, essential steps to be taken. The main purpose of this paper is to provide a model for seismic hazard analysis based on both main hazard of ground shaking and ground failure. Therefore, due to the large descriptive and location data, Software system designed and developed based on Geo-spatial information system and the ability to analyze the spatial data. This model to analyze the hazard analysis of ground shaking, in two attenuation relationship is used for the country, which according to the uncertainties involved in earthquake (magnitude of earthquake, focal depth and location of epicenter of the earthquake), this operation is selected at random each time and after each hazard analysis, the output of ground shaking (Peak ground acceleration, Peak ground velocity and peak ground displacement) is calculated. In the proposed model to analyze the hazard analysis of ground failure, based on area and proposed algorithms, three secondary hazard of earthquake (liquefaction, landslides and faulting) is analyzed. At this stage, given the liquefaction and landslide susceptibility can be calculated permanent displacement caused by liquefaction and landslide hazards for each component and each iteration of the simulation. All these steps are based on Monte Carlo simulation to considering the uncertainties are repeated 10 thousand times. Then averaging available outputs in the database, so that all failure modes be considered. This model could provide map of ground shaking and ground failure hazard for each parameter. In this paper, in order to get a better view this research, models was implemented and analyzed for the city of Neyshabour. That according to the selected scenario, the values of between 0.037 g to 0.48g PGA was calculated for the city. Liquefaction susceptibility in this city is mostly between 2 category of low and medium susceptibility. There was high susceptibility in the northwestern city. Landslide susceptibility in this city was evaluated mostly on the none susceptibility. Most displacement caused by liquefaction is related to the hazard of lateral spread that was evaluated mostly between 1 and 9 inches.