فهرست مطالب s. behzadi

Due to the increasing occurrence of natural disasters, importance of maintaining sustainable energy for cities and society is felt more than ever. On the other hand, power loss reduction is a challenging issue of active distribution networks (ADNs). Therefore, the distribution network operators (DNOs) should have a certain view on these two problems in today’s smart grids. In this paper, a new convex optimization model is proposed with two objective functions including energy loss reduction in normal operating mode and system load shedding minimization in critical conditions after the occurrence of natural disasters. This purpose is fulfilled through optimal allocation of distributed generation (DG) units from both conventional and renewable types as well as energy storage systems (ESSs). In addition, a new formulation has been derived to form optimal micro-grids (MGs) aiming at energy loss reduction in normal operating condition and resiliency index improvement under emergency situations. The developed model is implemented in GAMS software and the studies have been tested and analyzed on the IEEE 33-bus system. The results verify the effectiveness of the proposed method in terms of energy loss reduction as well as resilience enhancement in extreme operation condition following severe disruptions in the system.

Drought is a persistent and critical challenge that affects many countries around the world, including Iran. This natural phenomenon can have severe economic, social, and environmental consequences, making the study and prediction of drought an important focus for researchers and experts. The primary objective of this paper is to predict drought using an expert system and to find an appropriate behavioral model for this phenomenon across all provinces of Iran. Drought is a complex and multifaceted issue that can have far-reaching impacts. In Iran, where water scarcity is a longstanding concern, drought can exacerbate existing challenges and lead to significant disruptions in various sectors, such as agriculture, water supply, and energy production. Accurate and timely prediction of drought can help policymakers and stakeholders implement effective mitigation and adaptation strategies, thereby minimizing the adverse effects of this natural disaster.

This study utilized data related to drought in all provinces of Iran from 2009 to 2021. These data include various drought indices, such as precipitation, temperature, humidity, and climate change indicators. Using these data, the researchers developed monthly behavioral models of drought for each province, employing an expert system and artificial intelligence techniques. The researchers first examined the drought patterns and trends in each province to identify suitable behavioral models. This process involved analyzing the historical data and identifying the key factors that influence drought patterns in the different regions of Iran. By leveraging the expertise of domain experts and the capabilities of advanced analytical tools, the researchers were able to construct comprehensive behavioral models that capture the complexity of drought dynamics. The development of these monthly behavioral models for each province was a critical step in the research process. By modeling the drought patterns at a granular, provincial level, the researchers were able to account for the unique geographic, climatic, and socioeconomic characteristics of each region. This approach enabled the creation of tailored predictions that can be more effectively utilized by decision-makers at the local and provincial levels.

The results of this study demonstrated that the use of drought data from all provinces of Iran and the development of monthly behavioral models can indeed facilitate the prediction of drought in each province on a monthly basis. The researchers were able to produce twelve behavioral models for each province, representing the probability of drought occurrence in different months. These models can serve as powerful tools in managing and planning to combat drought at both the provincial and national levels. By providing accurate and timely predictions of drought, policymakers and stakeholders can make more informed decisions regarding water resource management, agricultural planning, and disaster response strategies. The findings also highlighted the importance of an integrated, expert-driven approach to drought prediction. By leveraging the expertise of domain experts and the capabilities of advanced analytical tools, the researchers were able to develop comprehensive and reliable behavioral models that capture the nuances of drought dynamics in Iran.

The findings of this study have significant implications for drought management and decision-making in Iran. By using an expert system and behavioral modeling of drought across all provinces, the researchers were able to achieve more accurate and timely predictions of this phenomenon. The linear model was selected as the best model, and an online web-based map was created to display the probability of drought for each province on a monthly basis. This web-based tool can serve as a valuable resource for policymakers, stakeholders, and the general public, facilitating informed decision-making and drought management at various levels. The availability of this information can lead to the reduction of the impacts and consequences of drought, enabling more effective planning and mitigation strategies to be implemented. The comprehensive and systematic approach used in this study can be replicated in other regions or countries facing similar drought-related challenges. By leveraging the power of expert systems, artificial intelligence, and behavioral modeling, researchers and policymakers can work together to develop robust drought prediction and management frameworks that enhance resilience and sustainability in the face of this critical natural phenomenon.

Traffic accidents are a major public health concern worldwide, causing significant loss of life and property damage. To reduce the number of traffic accidents, it is crucial to identify where and when recurrent accidents occur. These accidents often follow specific spatial and temporal patterns and may form clusters, representing areas of concentrated accidents within a geographical space. Therefore, analyzing traffic accidents in both spatial and temporal dimensions is essential for determining the most effective and sustainable solutions. Isfahan province is among the provinces in the country with high accident rates in Suburban areas. Previous research conducted in Isfahan province has predominantly relied on statistical methods and has not adequately addressed the spatial and temporal aspects of accidents.This study aims to address the gaps in previous research by determining the spatial and temporal patterns of urban traffic accidents in Isfahan province and visualizing these patterns using spatial statistical methods in a GIS environment. The novel aspect of this research lies in utilizing spatial statistical techniques to identify and analyze the spatiotemporal patterns of urban accidents in Isfahan province at different time intervals and intensity levels.

The spatial and temporal patterns of traffic accidents in Isfahan Province were investigated using suburban traffic accident data from March 2017 to March 2019. After collecting the relevant data, performing necessary preprocessing, and preparing the data, the spatial and temporal patterns of traffic accidents occurring on the main roads, highways, and freeways of the study area were analyzed and identified using spatial statistical methods such as the Average Nearest Neighbor test, Spatial Autocorrelation (Global Moran's I), and Optimized Hot Spot Analysis (Getis-Ord Gi* technique) at different levels in a GIS environment.

Since the aim of this study is to identify the spatial-temporal patterns of suburban traffic accidents in Isfahan Province, the spatial distribution pattern of accident events was first examined using the Average Nearest Neighbor and Spatial Autocorrelation (Global Moran's I) methods. The results indicated the presence of a strong clustering pattern in the traffic accident data during the study years in Isfahan Province. Then, an optimized Hot Spot Analysis was performed on the entire dataset of accidents using the Getis-Ord Gi* method. Subsequently, the analysis was conducted on the dataset of each level separately, considering different levels such as time of day, day of week, month, year, and accident severity level. The results of the Getis-Ord Gi* analysis at different levels showed that the majority of hot spots with a 99% confidence level are located on the routes leading to the provincial center, namely Isfahan City, as well as the neighboring populous cities. These areas experience the highest volume of traffic and congestion, and the accident density decreases significantly with increasing distance from the provincial center.

Based on the results of the Spatial Autocorrelation analysis of accidents and the hot spot maps at the studied levels, the results showed that accidents are clustered in some areas of Isfahan Province. Proximity to the provincial center and major populated cities has a significant impact on the concentration of traffic accidents in this region. The frequency of accidents decreases with distance from major urban centers. The results of this study and the insights gained about the spatial and temporal patterns of traffic accidents can be used to develop new strategies, guide transportation managers and stakeholders, make decisions, and take suitable proceedings to effectively improve the safety of accident-prone areas.

Rice, recognized as a strategic product for food security, holds a significant position not only in national economies but also globally. The importance of rice in meeting the dietary needs of populations and its role in achieving food security have led to a serious and substantial emphasis on this staple crop. In this regard, accurate and up-to-date data collection on the status of rice fields, especially information related to the quantity and quality of products, is crucial. Remote sensing technologies have been proposed as an efficient and effective solution in this context, enabling cost-effective data collection over extensive areas. Among these technologies, drones, due to their superior spatial resolution and higher precision in various monitoring tasks compared to satellites, offer relative advantages. This research employs an advanced approach called deep learning to estimate the cultivation area of rice seedlings or seedbeds using RGB images captured by drones in the Wufeng region of Taichung Province, Taiwan. The method leverages the capabilities of deep neural networks as an effective tool for analyzing complex data, achieving high accuracy in distinguishing various types of rice seedling or seedbed cultivation areas.

In this study, an advanced deep learning technique called DenseNet is employed for modeling and predicting the rice seedling or seedbed cultivation area in RGB images taken by drones. This method, utilizing complex algorithms and a set of processing layers, can extract high-level abstract concepts from the data. One unique feature of DenseNet is its use of a layer-to-layer algorithm instead of traditional layer concatenation approaches, resulting in reduced weights and parameters, as well as increased network efficiency. The ability of deep learning to process data in real-time immediately after image acquisition demonstrates the dynamic potential of DenseNet in quickly and accurately processing information. This capability allows real-time analysis and prediction of the rice seedling or seedbed cultivation area, providing the necessary information for optimal farm management.

The results obtained from this research demonstrate a confirmation of an accuracy exceeding 99.8% on validation data. This exceptionally high percentage indicates the remarkable capability of the DenseNet deep learning method in accurately estimating the cultivation area of rice seedlings or seedbeds. This high accuracy not only showcases the excellent performance of the model in identifying and predicting the rice cultivation area but also instills confidence in users. The presented model has successfully achieved precise detection and assessment of the rice seedling or seedbed cultivation area. This practical application provides valuable tools for farmers and farm managers to gain more accurate and timely awareness of their farm's status, facilitating better decision-making in cultivation and productivity.

This study convincingly shows the viability of employing drones in conjunction with sophisticated deep learning techniques for accurately estimating the cultivation area of rice seedlings or seedbeds. This approach proves feasible, especially in geographical areas similar to Wufeng in Taichung Province, Taiwan. The integration of drones and deep learning represents a notable technological leap in monitoring capabilities, offering substantial assistance to pertinent authorities involved in agricultural management and ensuring food security.

Today, urban management is one of the most important issues for decision makers and managers in urban areas. The problem of finding the shortest route, as one of the important issues in the field of urban management in order to reduce the travel time between two critical points, has always been the focus of many research fields such as urban management, transportation, communication, etc. During the past decades, the genetic algorithm has worked well in solving complex multi-objective optimization problems, but many genetic algorithms are only suitable for finding the optimal route in local networks. In these networks, if the number of points increases, algorithms will not be effective. The purpose of this research is to find a route in the street network of Tehran that has the least time, distance and cost. Therefore, a new method is proposed to solve the routing problem based on genetic algorithm, with the assumption that all lines in the network have positive weights.

The charactristics of the proposed algorithm is the variability of genetic algorithm operators, which is defined according to the network structure. Accordingly, the mutation operator in the genetic algorithm is defined based on the studied space and the distance between the start and end points. Integer coding is used to solve the problem, and therefore the points in this graph are named using integers and each person in the population is considered as an answer to solve the problem. The population size depends on the number of nodes in the graph and the length of each chromosome. The length of the selected strings is considered equal to the maximum number of nodes in the network, because there is a possibility that the best route is the route that passes through all the nodes. At the end, the proposed algorithm is implemented on the study area, which is a planar graph. The accuracy of the proposed method compared to the conventional genetic algorithm has been evaluated in three pairs of points.

Considering that the goal of solving the problem was to find the route that has the least weight, one combination operator and three mutation operators were presented in the proposed algorithm. This is despite the fact that in traditional genetic algorithms, only one mutation and combination operator is used. In this algorithm, the way to use mutation operators depends on the structure of the network and the distance between the start and end points. The use of the proposed genetic algorithm compared to the traditional genetic algorithm has been associated with a 16% improvement in performance, which shows that the proposed genetic algorithm reaches the solution of the problem faster. As shown in Figure 4, the best route is the route whose fitness function value is closer to one. The results of comparing the proposed method with conventional methods show 16% higher speed.

Considering that the initial assumption of this research was the positive weight of all the lines in the network, the mutation operator in the genetic algorithm was defined based on the studied space and the distance between the start and end points. The results showed that if there is a small search space, fewer points are needed and in order to generate the initial population, the nodes that are next to each other and closer to each other should be selected. In this way, the value of the fitness function of the people in the initial population increases and the answers become closer to reality. For future research, it is suggested that in order to generate the initial population, the points between the start and end points should be selected, and also the selected points should be near the connecting line between the start and end points, because when the weight of the edges of the network is the distance between the points, the best route is in the space between the starting point and the end point. It is also suggested to evaluate the performance of the network with several combination operators.

Traffic and its problems have become a big social issue in all societies today. Knowing essential factors that affect traffic, and the factors that aggravate traffic can be useful for managing the traffic. Nowadays, the problem of traffic forecasting has become a goal among different countries. If one can predict the traffic, he can prevent the waste of energy and time. Predicting the traffic situation and its behavior, especially in big cities, requires managing, planning, and spending time. Geographic Information System (GIS) is an important technology and science for spatial analysis. Since traffic is a spatial and temporal problem, this science can well manage the problem of traffic. In the field of traffic, people often look for a route that meets the criteria of being short, low cost, and consuming the least amount of energy. For this reason, traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that people prefer a long route with little traffic to a short route with heavy one. Thus, traffic affects many optimal route equations. Therefore, it is clear that the main criterion for choosing a route is the traffic situation on the relevant route. In this research, the collection of traffic data of Tehran province has been done using the proposed platform. Traffic data exists in real time, but the problem is that there is no platform for data collection. The lack of a suitable platform for storing traffic data has always been a problem that has challenged researchers in this field. Therefore, in this research, a method for collecting and storing traffic data on the web platform has been proposed. In this research, traffic images are first captured from web platforms. Then the intensity of the traffic is identified based on the color scheme. According to the position of the pixels in the image and the existing control points, the ground coordinates of all the pixels are calculated. Finally, the collected coordinates are stored in the database based on the standard spatial format. Finally traffic data is stored in the database instantly based on the current system. During this research, it was found that the point that has heavy traffic has heavy traffic in most hours of the day. It was also found that places with heavy traffic have a uniform behavior during different days of the week. In other words, it is rarely seen that points with different traffic loads have a significant difference in the traffic situation. In terms of accuracy, it can be stated that the collection of traffic data points has been done with acceptable accuracy in terms of location. The collected points are also the most compatible with other data in the database.

Climate phenomena such as quantity of surface evaporation are affected by many environmental factors and parameters, which makes modeling and data mining difficult. On the other hand, the estimation of surface evaporation for a target station can be difficult as a result of partial or complete lack of local meteorological data under many conditions. In this regard, satellite imagery can play a special role in modeling and data mining of climatic phenomena, because of their significant advantages, including availability and their potential analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms along with remote sensing data is inevitable.

In this research, we intend to study the ability of 11 machine-learning modeling algorithms to model data and surface evaporation phenomena using satellite imagery. We used two methods to prepare the database: PCA and its opposite method using standard deviation and correlation.

The calculation of the Root Mean Squared Error (RMSE) indicated that, in general, the use of the PCA method has a better result in preparing and reducing the dimensions of large databases for all methods of machine learning. The SEGPR model was ranked first with the least error (93.49%) in the Principal Component Analysis (PCA) method, and the Artificial Neural Network (ANN) model performed well in both data preparation methods (93.42, 93.38), and the Classification-Tree-Coarse model had the highest error in both methods (92.66, 92.67).

Consequently, it can be said that by changing the methods of database preparation in order to train models, the modeling results can be changed effectively.  

Reproduction biology and feeding habits of black mouth croaker, Atrobucca nibe, was investigated monthly from September 2013 to August 2014 in the Oman Sea. The specimens were collected by bottom trawl in the Northwest of Oman Sea. A total of 637 fish specimens examined, 284 males and 353 females were sexed. The average sex ratio was F: M= 1.24:1, showing dominance of females over the males. Spawning season of A. nibe was mostly observed in June and October but spawning might be occurred in all year long . The mean absolute fecundity was calculated 97008±55553 eggs, with a moderate linear relationship between absolute fecundity and total length (Fecundity= 10802TL-280712, R2=0.7624). The mean length at first sexual maturity (Lm50 %) was 35 cm for females. A. nibe is an abstemious feeder according to the high vacuity index (VI=86.5±16.8 %). Skinny cheek lanternfish (65%), Japanese threadfin bream (13%) and deep-sea shrimps (11%) were the frequent food items of this species. Results showed that  bony fishes were consumed by all size-classes, skinny cheek lanternfish (42.1%) and Japanese threadfin bream (79.4%) were more pronounced in size-class of 34-38 cm, and also  crustacean were more frequent (29%) in 22-26 cm size classes.

It is a fact that people are often looking for a way that combines the parameters of shortness, low cost, and low energy consumption. Hence traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that people often prefer along with low traffic than a short one with heavy traffic. Therefore, it is clear that the main criterion for choosing a route is the traffic situation in the relevant route. Traffic has become a major social problem in all societies today. Understanding the causes of traffic and its aggravation parameters can reduce traffic problems. Meanwhile, the issue of traffic forecasting has become a goal among different nations. Since traffic can be predicted, it is possible to avoid wasting energy and time, which has become a crisis in metropolises today. But predicting traffic conditions and behavior, especially in large cities, requires management, planning, and using technologies such as GIS. In recent years, the urban transportation network has become more complex in modern societies. The reason for this is the creation of different infrastructures with the motivation of creating more convenience for the movement of citizens. The high complexity, multi-layered nature, and multi-structured nature of the urban, transportation network do not make it easy for citizens to move, and these factors may even confuse citizens more than just moving from one place to another. There is a direct link between transportation and traffic. So far, urban plans have been made to improve the traffic situation. The variability of the parameters affecting the traffic situation and its direct impact on the traffic problem has always been a big problem for different communities. Therefore, these parameters should be identified and the role of each of them on the traffic situation should be measured. Then it is possible to improve the traffic situation. To achieve this issue, the role of Geographic Information System (GIS) to solve problems that have a specific spatial and temporal dimension (such as traffic) should not be overlooked. This highlights the need for the present research to collect traffic data. If the goal of the research is achieved, it will save time, money, and energy at least. To measure traffic behavior in metropolitan areas and to achieve up-to-date traffic data, there is a need to provide and use methods to analyze traffic behavior. By achieving this goal, transportation can be prospered and the economic burden can be reduced on different communities every year. For this reason, solutions for traffic forecasting should be sought. In the meantime, the use of science called neural networks can be very practical. In this research, first, a system was designed to collect the traffic data required for the research. The issue of access to traffic data has always been a problem, which is concerned in this area. Therefore, in the present study, first, by designing a system for collecting traffic data, the desired problem was solved. In the next step, the collected traffic data is called and normalized. Then the error rate was calculated using test and training data. At this stage, the error rate was 72%. In the next step, traffic data analysis was performed using a combination of baseline and wavelet neural network, and the error rate was then calculated. The results show that using wavelet transforms is more accurate, but the error values ​​were calculated using test and training data, 28% due to the smaller number of inputs. In other words, the desirability rate was about 72%. Finally, the collected traffic data were optimized using optimization algorithms and the best point was calculated with the least possible error for each optimization algorithm.

The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role in modeling and data mining of climatic phenomena as a result of their significant advantages, including their availability and analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms using remote sensing data is inevitable. In this study, 7 well-known machine learning algorithms are applied with different initial data to show that satellite images are able to estimate the combined indices more accurately. A new index (HT) is also defined by combining the quantities of relative humidity and temperature. Then, machine learning algorithms are trained for each of these three quantities. For each of the temperature and relative humidity quantities, four optimal bands were selected using the PCA method, then a combination of these optimal bands was determined for the HT index. Two criteria are used to validate the results Root Mean Square Error (RMSE) statistic and comparing the map of the interpolation method with the result of this study. RMSE values show that satellite imagery could have a high ability to model and estimate composite indices. Classification-KNN-Coarse and Ensemble-Bagged Trees with accuracy of 79.8626 % and 84.9281% are identified as the best machine learning methods for temperature and relative humidity, while the best accuracy to estimate the HT index is 92.8792% for Matern 5/2 GPR. Therefore, it can be said that by changing the methods of database preparation, the modeling results can be changed effectively in order to train models.

According to the geographical location of Iran, it enjoys both arid and semi-arid weather, which has been the cause of water shortage problems. Therefore, water has been one of strategic importance in Iran. When the population dramatically increased in ancient Iran, the demand for supplying water also increased simultaneously and brought about the need for the invention of a new way for supplying water from the ground by using gravity. This invention was named Qanat, and it has an important role for managing groundwater in arid and semi-arid areas like Isfahan. Qanat is a sustainable way for water supply in many regions. As qanat is a cultural heritage which is still prevalent in some regions of the country, it has been of great importance not only in the history of Iran but also recognized around the world as an unprecedented invention. The unparalleled source of this ancient invention and its popularity has led to their preservation in Iran and also made it famous around the world. In this article, due to the importance of its cultural heritage, attempts have been made to find the routes of the qanat in Isfahan using available spatial layers such as flood way, stream, ditch, watercourse, bush, etc. based on fuzzy logic method. To this regard, three different scenarios of fuzzy logic rules are proposed and consequently three paths are obtained for each group of fuzzy rules. Then, the explored routes are compared with the qanat route that was found by geophysics institute in Iran. The similarity of the most desirable map with the actual one was 73 percent.

Air pollution is one of the most problems that people are facing today in metropolitan areas. Suspended particulates, carbon monoxide, sulfur dioxide, ozone and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. The goal of this study is to propose a spatial approach for estimation and analyzing the spatial and temporal distribution of ozone based on GIS analysis and multi perceptron neural network. In the first step, by considering the accuracy of different interpolation methods, IDW method was selected as the best interpolation method for mapping the concentration of ozone in Tehran. according to the daily data of these pollutants, the daily, monthly, and annual mean concentrations maps were prepared for years 2015, 2016 and 2017. According to the results, it can be said that the highest concentrations of ozone are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. According to the data of ozone pollutants in year 2018, the accuracy of neural network for hot and cold days of year were about 68% and 77% respectively. Therefore, it can be said that the meteorological parameters of temperature, wind speed and direction, and precipitation are significantly related to the concentration of O3 pollutant.

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

Land subsidence is one of the phenomena that has been abundantly observed in Iran's fertile plains in recent decades. If it is not properly managed, it will cause irreparable damages. So, regarding the frequency of subsidence phenomenon, the evaluation of the potential of the country's fertile plains is necessary. Towards this, the present study is formulated to assess the vulnerability of the Tehran-Karaj-Shahriyar Aquifer to land subsidence.

The vulnerability of Tehran-Karaj-Shahriyar Aquifer was determined using the GARDLIF method in a Geographic Information System (GIS) environment. Seven parameters affecting ground subsidence including groundwater loss, aquifer media, recharge, discharge, land use, aquifer layer thickness, and the fault distance were used to identify areas susceptible to land subsidence. Then, they were ranked and weighted in seven separate layers. In the next step, the subsidence location and rates were obtained using the differential interferometric synthetic aperture radar (DInSAR) method. The weights of the input parameters of the GARDLIF model using the subsidence map obtained from the DInSAR method and the particle optimization algorithm (PSO) were then optimized. Accordingly, the subsidence susceptibility map was generated based on the new weights.

The results showed that by increasing correlation coefficient (r) from 0.55 to 0.67 and the amounts of Coefficient of Determination (R2) from 0.39 to 0.53 between the subsidence index and the obtained subsidence in the aquifer, the optimization of weights applied by the PSO algorithm is more capable for evaluating the land subsidence than the map created by GARDLIF. It was also found that the central parts of the study aquifer had the largest potential for land subsidence.

Urmia lake due to the presence of various species of wildlife, species of vegetation on the islands, create a natural balance in the Azerbaijan region,  tourist, recreational and social value, medical value, reserve of the Bio sepehr and as well as a wetland of international importance is special. Over the last few decades , use remote sensing technology to detect trends such changes various researchers have drawn attention to themselves. Factors that have caused Urmia lake will be in such a situation  is varied. But in general, they can be divided into two categories :The factors that played a role in humans includes free use of water resources , agriculture unbridled development around the lake, and environmental factors like climate change , which according to the reduction of heavens and evaporation of Urmia lake water And reducing the flow volume and reduce annual temperature the lake ecosystem has been affected. Study of meteorological parameters of Urmia lake and investigation of its level changes in order to apply water resources management is important. Recent studies show which level and volume of lake water relatively decreasing. Urmia lake water level from 1992 to 1997 significantly increased and decreased from 1997 to 2009 and has remained almost constant since 2010. As a result, to rebuild the lake and managing the water resources of this lake is necessary, the role of effective parameters is determined. Therefore, neural network method was used in this research,meteorological parameters such as evaporation,temperature, precipitation, and annual amounts of groundwater abstraction of  wells around the Urmia lake and the amount of water entering the lake, between 1997 and 2011, as input parameters And the annual altitude and area of the lake water entered the neural network as output parameters. In this research, the Levenberg rules were used to train the network. After training model by meteorological parameters, it was determined that the neural network model approximates the data in a perfectly accurate and accurate manner. It can also be predicted that changes in height and area occur by changing each of the parameters. This network estimates the lake area of Urmia at 3% error and 97% accuracy and lake level of 0/8 m. The correlation coefficient of the removal was obtained with the height and the range of -0.4. The correlation coefficient of precipitation with 2 dependent parameters was obtained +0.15 Input flow rate of +0.4.  After reviewing the model, it was found that the removal parameter from underground wells and the Input water volume into the lake compared to other parameters have a more significant effect on altitude and area. The results indicate that water use for agriculture and harvesting of water resources have increased And also the crops that are grown are products with a high water consumption pattern And also the water stored behind the dams has reduced the inflow to the lake.

Urban transportation is one of the most important issues of urban life especially in big cities. Urban development, and subsequently the increase of routes and communications, make the role of transportation science more pronounced. The shortest path problem in a network is one of the most basic network analysis issues. In fact, finding answers to this question is necessity for higher level analysis. In general, shortest path solution methods using optimization algorithms are divided into two categories: exact and approximate algorithms. In exact algorithms, achieving the optimal solution requires time, and consequently more cost. On the opposite side, there are some approximate algorithms that work in a short period of time. Meta-heuristic algorithms are among approximate algorithms that are capable of finding optimal or near-optimal solutions in a reasonable period of time. The method used in this study is to solve the shortest path problem with the combination of Genetic meta-heuristic (GA) and Tabu Search (TS) algorithms. GA is inspired by genetic science and Darwin's theory of evolution; it is based on survival of the highest or natural selection. A common use of genetic algorithms is to be used as an optimization function. In GA, the genetic evolution of living things of life is simulated. Inspired by the evolutionary process of nature, these algorithms solve problems. GA forms a set of population (solutions), then it achieves an optimal set by acting some possess on the correct set. To solve a problem by genetic algorithms, it is necessary the problem is converted to the specific form required by GA. On the other hand, TS algorithm is not population-based. It obtains an answer, then it tries to direct the answer to the optimal solution by applying a series of operators. This algorithm is highly similar to the Simulated Annealing algorithm. In this paper, for solving the shortest path problem, a series of geometric pre-processing on the network is done to generate a search area around the source and destination nodes. In the proposed algorithm, the cost function is defined as a complex number, which the real part shows the sum of the weight of the real edges, and the imaginary part denotes the number of virtual edges. The innovation of this research is about applying Tabu Search algorithm in mutations process of genetic algorithm. The proposed method overcomes the inappropriate response of the pure genetic algorithm in terms of the final weight of the path especially the large networks. In order to evaluate the efficiency of the proposed algorithm, the algorithm was implemented on a real directional network which is part of Tehran city road networks including 739 nodes and 1160 edges. The results show that in the proposed algorithm, the length of the path is as close as possible to the solution obtained from the definitive Dijkstra’s algorithm. This algorithm predicts approximately the final path length of 5% more than Dijkstra’s algorithm. But in terms of running speed, it is 5.12 times faster than the Dijkstra’s algorithm. In comparison with the pure genetic algorithm, the proposed algorithm is 9% shorter in average in terms of path length. And about the running time, the speed of the proposed algorithm is approximately equal to the pure genetic algorithm. Regarding to repeatability, the proposed algorithm also shows 25.36% of repeatability.