genetic algorithm

Flood is one of the most destructive natural disasters with social, economic and environmental consequences. Therefore, comprehensive flood management is necessary to reduce the effects of floods on human life and livelihood. The main goal of this study is to investigate the application of the maximum entropy model (Entropy Maxent) in R software for flood susceptibility mapping in Fars province (Jahrom urban basin). First, by using the information of natural resources of Fars province and field visits, flood-prone points (50 points) were determined. In the next step, environmental variables such as altitude, slope, and distance from the river, drainage density, average annual rainfall, land use, soil type, and geology were selected by performing the multiple collinearity test, and vegetation cover and topographic humidity index were removed. Among the selected environmental variables, the three factors of height distance from the waterway and land use have had the greatest impact in the modeling process. After that, the receiver operating characteristic curve (ROC) was drawn for the flood sensitivity map, and the value of training data (0.943) and test data (0.932) was obtained. In the following, the model was optimized and upgraded using the genetic algorithm. As a result, this flood susceptibility map can be useful for researchers and planners in flood mitigation strategies.

Resilience has been defined as "a process of ability, or the result of successful adaptation despite threatening conditions", which plays an important role in dealing with the stresses and threats of life and its adverse effects. Also, resilient cities are vulnerable to events. mitigate extremes and respond creatively to environmental, economic and social changes in order to increase their long-term sustainability. Therefore, the purpose of this research is to predict the resilience of Qaemshahr city against natural hazards. The method used in this research is to use GIS software in order to determine points with more resilience and use genetic algorithm (GA) and particle swarm intelligence (PSO) methods and their programming in MATLAB environment, in order to Using it, the most important components of the city's resilience and the effective strategies to strengthen resilience will be determined. The present research was conducted in two stages, in the first stage, first, using the AHP model, the degree of resilience of urban areas against the 3 hazards of floods and earthquakes. and the landslide was determined and after that the final risk map (resilience level) was prepared by combining all the mentioned components. In the second stage, in order to determine the most important components of the city's resilience, this time 32 indicators of the resilience components were compiled based on 4 criteria (physical-spatial, economic, social, institutional) and analyzed using the genetic algorithm method. Finally, 3 points of the city (Shahid Watani Stadium, Telar Park, Siraj Park) were selected and prioritized as areas with more resilience. Then, in order to confirm and compare the research findings, the PSO method was used by the genetic algorithm (GA). As a result, it can be said that there is a significant difference in terms of resilience among the city's regions, that the increase in the resilience of Qaimshahr city The first stage requires strengthening and modifying the structures of the natural environment and then maintaining the safe environment.

The increasing demand for sustainable food consumption as well as the change in the consumption pattern has led to efforts to improve the food distribution process. This is to speed up service delivery and prevent the spoilage of perishable materials. Among the most significant topics in the food supply chain is perishability, a phenomenon that occurs in certain categories of products such as fruits, vegetables, and dairy products. Perishability refers to the property in which a product loses its commercial value and usability after a certain period. However, meeting the general needs of citizens, especially the supply of food, is one of the most significant axes of urban service activities on the city's economic platform. In addition, the provision of comfort and well-being for residents depends on the proper establishment, optimal distribution, and sufficient variety of products offered in shopping centers. Day markets as well as fruit and vegetable fields provide fast and appropriate daily needs for residents. In addition, choosing fast and reliable routes for food distribution in the city is one of the other significant and influential factors in providing quality services. It should also be noted that in vehicle routing problems (VRP) related to food products, routes for vehicles must be created that match the schedules of some stores to deliver products.

To optimize the fruit and vegetable distribution routes between the fruit and vegetable fields and Shahre-ma stores in Mashhad, this research will use genetic algorithms and particle swarm algorithms. This research will have the aim of optimizing distribution time, which was not addressed in previous research. This research presents its innovation by considering a three-hour time limit in the problem-solving algorithm. Genetic Algorithm (GA) is a learning method based on biological evolution and influenced by the hypothesized mechanism of natural selection in which the fittest individuals in a generation survive longer and produce a new generation. And in this article, it is implemented in such a way that the algorithm itself determines the most appropriate number of vehicles. The number of vehicles should be such that distribution among all stores is done in less than three hours and five minutes in each store. There should be a stop. And if distribution among all stores is not done in less than 3 hours, a new vehicle will be added to the number of vehicles. Also, particle swarm optimization (PSO) is a technique inspired by the behavior of birds when searching for food. In this research, the data collected include the location of Shahre-ma stores and the fruit and vegetable square in Mashhad city. These data were prepared from the information of Mashhad municipality. Also, to implement these algorithms, MATLAB software has been used. Network analysis has been done to determine the distance between Bar Square and Shahre-ma stores in ArcGIS software using network analysis.

This research proposes several hypotheses, including that the maximum optimal time is 3 hours and products should be distributed by 7 am in all places. Also, city traffic is uniform from 4 to 7 in the morning and the same product package is distributed in all stores. Comparing the results of two genetic algorithms and particle swarm shows that the genetic algorithm has a higher efficiency in optimizing the distribution path of fruits and vegetables. Because the time of the four routes derived from the genetic algorithm is approximately 92 minutes, 84 minutes, 80 minutes, and 82 minutes respectively. The total length of all routes is 127 km and 779 meters and the total time of all routes is 338 minutes. And the time of the four routes obtained from the particle swarm algorithm is approximately 102 minutes, 103 minutes, 89 minutes, and 91 minutes respectively. The total length of all routes is 175 km and 390 meters and the total time of all routes is 385 minutes. And in total, the times obtained for four vehicles in the genetic algorithm were 47 minutes less than the particle swarm algorithm. In addition, the total length of the paths in the genetic algorithm was 47 km and 611 meters less than the particle swarm algorithm.

Conclusion:

The genetic algorithm was able to achieve the optimal solution by evaluating the objective function 12,000 times. This is 2,900,000 in the particle swarm algorithm. Accordingly, the time required to reach the optimal solution differs significantly between the two algorithms.

Due to the importance of manufacturing and production systems issues in modern businesses, in recent years, many magazines and researchers have focused their research on this field. Manufacturing and production is an emerging pattern in which production resources are hardware (handling materials, equipment, tools and machines, computers), software (computer-aided design, computer-aided production) and production capabilities (ability design, production, maintenance, repair, management, simulation, optimization) is virtualized and available to users in the entire manufacturing and production cycle and allows the joint use of production systems and resources produced at the global level. Manufacturing and production based on new technologies is a solution that enables users to receive their requests in different layers with optimal timing. Therefore, the main goal of this research is to provide a mathematical model to optimize the production output function in dynamic manufacturing and production systems. For this purpose, the researcher first conducted a comprehensive and complete study of the research literature, and after collecting the information, he selected and developed the initial model, and in the second phase of the research, he started by obtaining statistical information and data from the relevant statistical community, to build the model. Its original and initial test was done. Since the problem of optimizing the production output function in dynamic manufacturing and production systems is included in the NP-hard category, it means that for such problems, a quick and feasible solution has not been found in a reasonable time from the genetic algorithm and ant colony in MATLAB software was used and through the tools and methods of solving and analyzing them, the questions of the current research were given a suitable and optimal answer.

Considering the harms of air pollution on human health and the environment, it seems necessary to reduce and solve this problem based on accurate knowledge of pollutants and criteria affecting it and identifying polluted areas. Therefore, using mathematical models in the form of machine learning is an optimal and cost-efficient approach to air pollution modeling. This research is applied in terms of purpose and its method is descriptive-analytical. The novelty of this research is presenting a new combination approach to determine the effective criteria for predicting the amount of air pollution. Therefore, the purpose of this study was to evaluate and compare the capabilities of two machine learning models, namely Support Vector Machine (SVM) and Random Forest (RF) in combination with Genetic Algorithm (GA) to predict air pollution in Tehran. The data used in this research include particulate matter and gaseous pollutants in Tehran in 2020, which was obtained from Tehran Traffic Control Company. MATLAB and ArcMap software were used to analyze the data. The value of coefficient of determination (R2) obtained from the combined RF-GA method was 0.997, which indicates the high compatibility of this model with the data of this study. Moreover, the Root Mean Square Error (RMSE) value from the combined RF-GA method was 0.153, which indicates high accuracy of this model. Based on the data obtained from Tehran Traffic Control Company, the results of the RF method indicate the appropriateness of selecting the model to estimate the amount of air pollution in Tehran.

In the beginning of spring, floods are the most important geomorphic hazards in Iran, destructing propertiesas and human lives. Aland Chai basin, located in Khoy County (northwest Iran), is also known as one of the basins with high potential for flood hazard due to its special geographical situation. This study tried to model spatial variation in flood hazard susceptibility in this basin using the ensemble model, FURIA-GA-LogitBoost. For this purpose, 13 effective parameters of flooding including lithology, soil hydrological groups, NDVI, land use, slope, aspect, elevation, distance to the river, river density, precipitation, topographic wetness index, stream power index, and sediment transport index were used. WEKA software was used to implement the research model and the final flood hazard susceptibility map was prepared. The study found that downstream areas of the basin have a high flood hazard susceptibility. These areas contain the most important human settlements (Khoy city) and agricultural lands and flood as a geomorphic hazard can seriously damage them. Considering the ROC curve and area under the curve (AUC), it was found that the FURIA-GA-LogitBoost model performed well in the preparation of flood hazard susceptibility map with coefficients of 0.861 and 0.895, respectively, in training and validation data.

Land use subject and optimization patterns are one of the basic urban issues that human beings are always offering models and solutions to maximize the use of land and its capabilities in a variety of applications. On the other hand, optimizing medical land use will facilitate individuals' access to services and it Cause spatial justice all around the city. The main purpose of this research is the location optimizing of medical land use and, in particular way hospitals in urban areas.In addition, this goal in terms of locating criteria is responsive to the needs of citizens and being in a good position.The case study for this research was the 17th district of Tehran. District 17 has a small size, high population density, and small grain, which means this district is facing many problems with providing service levels. Also, the lack of public services has become one of the main factors in reducing the population in this district. Moreover, medical land use as one of the most basic service levels in the region has 0.16 m per capita, which is lower than the standard per capita and the per capita of Tehran city. The data of this study were collected on a map of the city and analyzed by the Geographic Information System (GIS) and the application of the Boolean logic method, by integrating the information layers on the final map. Then the appropriate hospital deployment areas are determined. Ultimately, by genetic algorithm (GA), the optimal and best answer for solving this problem has been derived by defining multiple functions and using MATLAB software. The results of an analysis of the current situation of medical land use in the 17th district of Tehran indicate that the current spatial distribution of medical land use is showing a lack of proper access for citizens. This area has a severe shortage of medical land use that causes the optimal location of the medical land use and specifically the hospital to be on the main agenda of this research. In conclusion, the results of the research show that the proposed method, while considering populations, neighborhoods, land characteristics, and access, finds the best place for therapeutic use. Therefore, it can be effectively used to locate optimal therapeutic use.

Among the types of airborne particles, particles with a diameter of less than 10 microns have many adverse effects on human health. Meteorological parameters and the movement of a large volume of vehicles are considered the most important modulating factors in the distribution and concentration of atmospheric pollutants. In this study, in order to predict the concentration of PM-10 pollutant during a long-term interval in Tehran city, GA-ANFIS hybrid model was used. Wind speed, wind direction, temperature, relative humidity and traffic volume were considered as inputs and pollutant concentration PM_10 as the output of the model. The results of the calculation of the performance indicators showed that the combined GA-ANFIS model provides a better framework than the ANFIS model in predicting the pollutant concentration PM_10. In order to evaluate the spatio-temporal patterns of PM_10 pollutant concentration and to identify hot and cold spots in Tehran city, local Moran's statistic and Ard-J Gettys statistic were calculated. The results showed that there is a high level of clustering of PM_10 pollutant in Tehran (with 95% confidence level). The clusters of PM_10 have divided the city into two northern and southern parts so that most of the cold spots in the northern half and the hot spots in the south have spread to the center of the city

Buildings are one of the main pillars of economic and social development of countries that consume a large part of energy and natural resources. The purpose of writing this article is to calculate the energy consumption of a building in one year, determine the energy label and then optimize some of its features in order to reduce construction costs and reduce energy consumption of the operation phase. Is. The study area is common residential buildings in Tehran. The research method to achieve this goal is as follows: First, according to the licensing statistics of Tehran Municipality, a 5-storey southern building was selected on a land with an area of ​​320 square meters and an infrastructure of about 1100 square meters, which represents a large number of buildings in Tehran. Be. Then, by simulating the building considered in Builder Design software, the amount of gas consumption is 145.53 kWh per square meter, electricity consumption is 81.25 kWh per square meter and a total of 226.79 kWh per meter. The square was calculated per year. With this consumption, according to standard 14253, the building receives energy label C. Then, with the aim of reducing the cost of construction and energy consumption during operation, it was optimized with a genetic algorithm. Research variables, type of exterior wall (pottery or Leica), exterior (stone or brick), type of window glass (plain or low emission), type of gas between the layers of window glass (air or argon) and the ratio of the window to the south front surface (15 % -30% -45% -60%) were built. The results indicate that the best possible scenarios for the exterior facade, stone, low-emission double-glazed windows with argon gas, the outer wall of the pottery with a window-to-south front ratio of 22.5% or Leica, with a window ratio At the level of the southern front, it is 37.5%.

Nowadays, demand for water is increasing especially in arid and semi-arid regions (e.g., Iran) due to population growth, economic development, higher standard of living, and changes in consumption patterns. Hence, optimal management of water resources in these areas is essential. Furthermore, climate change and increasingly extreme weather events have caused a surge in natural disasters (e.g., floods) over the past 50 years in arid and semi-arid regions. Thus, forecasting and modeling of runoff data is extremely necessary for planning and managing of water resources. Water flow forecasting plays a key role in flood reduction, reservoir optimization, and reservoir management. These models are mostly developed and applied for simulation and prediction. Therefore, different types of forecasting methods have been proposed over the decades including: Box and Jenkins (SARIMA), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Genetic Algorithm (GA) models. Forecasting hydrological reactions invariably involves uncertainty. So far, numerous studies have been performed to improve the reliability and accuracy of hydrological forecasts, resulting in reduced risk error. Therefore, the main objective of current research was to use artificial intelligence methods consisting of ANN, ANFIS, GA, and SARIMA models to predict monthly runoff data and also select the best model for the efficient management of water resources in the Gharasou River basin.

Gharasou river basin with an area of 1624 square kilometers is located in the west of Golestan province and has an important role in providing water resources required in this province. In this research, to model and forecast the runoff process, the monthly runoff time series of 4 hydrometric stations of Pol-Tuskestan, Naharkhoran, Ghazmahale, and Siah-ab over Gharasou River basin were used for a period of 36 years (1982-2018). The time series homogeneity was examined using the Chow`s method. Runoff data are time dependent, so initially these data were arranged in time series. After sorting the data, four models consisting of Box and Jenkins (SARIMA), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Genetic Algorithm (GA) models were used to forecast monthly runoff. To increase the prediction accuracy of other methods, the far time series of monthly runoff were first ignored based on to neural network method and then the number of effective years for modeling was determined. Later, the monthly runoff was forecasted for the next 12 months using four models consisting of SARIMA, ANN, ANFIS, and GA. Lastly, based on the forecasted values and using MAD, RMSE and MAPE indices, the accuracy and precision of SARIMA, ANN, ANFIS and GA models were compared. Modeling and forecasting were done using Minitab, R and SPSS software packages.

Based on the type of distribution of monthly runoff and the presence of zero data, log(1+Yt) conversion was used in the models to stabilize the variance. The results according to the autocorrelation diagrams revealed that the time series in all stations have seasonal trend with a period of 12 months. Then, the monthly runoff of the next 12 months was forecasted using four models including SARIMA, ANN, ANFIS and GA. Model validation results using three indicators of MAD, RMSE and MAPE revealed that the ANN model in the three hydrometric stations of Naharkhoran, Pol-Tuskestan and Siah-ab had the best performance. In these three hydrometric stations, after ANN model, the ANFIS model has been selected as the most suitable model. However, the performance of these two models has been very similar. In the Ghazmahaleh hydrometric station, two models of ANFIS and ANN had the best performance, respectively. In this study, it was also found that in four selected hydrometric stations, the GA model had a good performance after the two models of ANN and ANFIS. Although SARIMA model performed very well in identifying the trend of monthly runoff changes, it had the weakest performance among the methods. The forecast data using SARIMA model were overestimated compared to the actual data for March and April, but in other months, the forecast data using this model were relatively appropriate.

In this research, to model and predict the monthly runoff process, four models including SARIMA, ANN, ANFIS, and GA models were used for four selected hydrometric stations in Gharasou River basin. The results of model validation using three indicators of MAD, RMSE and MAPE showed that the ANN and ANFIS models had the best performance among the four models used. It was also found that time series of runoff data in hydrometric stations have undergone structural changes due to the physical and climatic alterations in the upstream of rivers. Therefore, the distant past of time series may cause deviations in modeling and forecasting results. To overcome this problem, making use of an algorithm to select the number of effective years in modeling and forecasting can be useful. Artificial neural network provides a suitable criterion for selecting the number of effective years due to its high accuracy in modeling and forecasting. Based on the effective years identified by this model, other models can be modified and provide more appropriate input data for forecasting from other models.

With the development of urbanization, cities have become the focus of attention and the demand for natural resources and attention to their global environmental impact has increased.Urban planning and urban planning activities have increased the pressure on nature and weakened its resilience, which has often had devastating consequences for cities and their inhabitants.Therefore, the purpose of this study is to model the degree of resilience of the city against natural hazards with emphasis.The method used in this study is to use GIS software to determine the most resilient points and to use the methods of genetic algorithm(GA)and particle swarm intelligence(PSO)and their programming.It is in MATLAB environment, to use it to determine the most important components of urban resilience and effective solutions to strengthen resilience.Against the flood was determined and then the final hazard map was prepared by combining all the mentioned components. In the second stage, to determine the most important components of resilience in the city, this time 32 indicators of resilience components based on 4 criteria(physical-spatial, Economic,social, institutional) and was analyzed using the method of genetic algorithm, which finally3 points of the city, respectively(Stadium Sh Hid Watani, Tlar Park, Siraj Park)were selected and prioritized as the most resilient areas. Then, in order to confirm and compare the research findings by genetic algorithm(GA)PSO method was used.In the final stage It is suggested that increasing the resilience of Ghaemshahr city in the first stage requires strengthening and modifying the structures of the natural environment and then maintaining a safe environment.

Drought, as a climate threat, has a significant impact on the environment and, consequently, on humans and other living organisms. Therefore, monitoring and predicting this phenomenon is necessary. This study, to examine and evaluate the drought forecast in the west and north -west of Iran, including Hamedan, Kermanshah, Kurdistan, West Azerbaijan, East Azerbaijan, Ardabil, Zanjan, Qazvin, Ilam, Markazi, Gilan, and Lorestan, have been used multivariate standardized drought index (MSDI) and methods based on artificial intelligence. To predict the values of this index in the study area, monthly rainfall, and soil moisture, as the input, and the calculated amount of MSDI, as output, was applied. The grid data on precipitation and soil moisture for a period of 36 years (1980 -2016) were obtained from the MERRA database. The results of monthly drought analysis based on these data showed that the most severe drought in the study area occurred from March to October and the main focus of this phenomenon are Lorestan provinces, especially Ilam and Kermanshah. The findings were following seasonal and annual maps. According to the MSDI index classification, no severe drought was observed in the study area and the droughts were in the middle class. The results of artificial neural network modeling also showed that the use of artificial neural networks, in general, has an appropriate ability to simulate properly. Among the algorithms used to optimize the artificial neural network, the genetic algorithm has the best performance compared to other methods in predicting drought.

Resilient cities are cities that support the development of wider resilience in economic and social life, its infrastructure, and institutions; resilient cities also reduce vulnerability to severe events and creatively respond to environmental, economic, and social changes to increase their long-term sustainability. Therefore, the purpose of this study was to model the city's resilience to hazards.

The method used in this research is to use GIS software version 10.3 in order to determine points with greater resilience and to use genetic algorithm methods and particle swarm intelligence and their programming in the MATLAB environment in order to use it to determine the most important components of city resilience and effective strategies to strengthen resilience are identified.

The present study was conducted in two stages. In the first stage, first, using the analytical hierarchy process model, the degree of resilience of urban areas against landslides was determined and then, by combining all the mentioned components, the final hazard map was prepared. In the second stage, to determine The most important components of resilience in the city This time, 32 indicators of resilience components based on 4 criteria (physical-spatial, economic, social, institutional) were compiled and examined using a genetic algorithm, which finally 3 points of the city, respectively (Talar park, Shahid Watani Stadium, Siraj Park) were selected and prioritized as areas with more resilience. Then, to confirm and compare the research findings by genetic algorithm, the Particle Swarm Intelligence method was used.

There is a difference in terms of resiliency among the city areas, and increasing the resiliency of Qaimshahr city in the first stage requires strengthening and modifying the structures of the natural environment and then maintaining the safe environment.

Flood is a disaster which causes a lot of economic damages to farmlands, forests, gas and power transmission lines, roads, engineering structures, and buildings. There are numerous floods in the northwest of country at the beginning of spring and the start of spring rains, which in most cases results in heavy damage [4]. The aim of the present study was to prepare a map of spatial variations in flood risk susceptibility in the Aland Chai basin located in West Azerbaijan province and Khoy city. To achieve this aim, 13 effective parameters in the occurrence of this phenomenon have been used. These parameters include lithology, soil hydrological groups, NDVI, land use, slope, aspect, elevation, distance to river, river density, precipitation, topographic wetness index, stream power index, and sediment transport index.Study areaAland Chai basin is located between 38°- 30¢-14² and 38°- 48¢-22² N and between 44°- 15¢- 13² and 45°- 01¢-02² E in the Northwest of Iran and the Western Azerbaijan province. This basin is one of the sub-basins of the Aras River basin to which surface water flows after joining the grand Qotour River. Basin elevation variations are from 1093m in the Aland Chai River bed to 3638m above sea level in the Avrin Mountain [4].

The following data, software, and methods were used to analysis flood risk susceptibility and prepare flood risk maps in the study area:- A frame of Landsat 8 satellite image OLI scanner with path of 169 and row of 33, in 30m spatial resolution- Geological maps in 1:100000 and 1:250000 scale from Khoy and Dizaj- Topographic map in 1:50000 scale from Khoy city- Digital Elevation Model (DEM) in 12.5m spatial resolution- ArcGIS software to generate maps- ENVI software for land use mapping- WEKA software to data miningThe new ensemble model of FURIA-GA-AdaBoost have been used to investigate the role of parameters in the occurrence of floods. FURIA is a fuzzy rule-based classification method, an extension of the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) rule learner (Cohen, 1995), introduced by Hühn and Hüllermeier (2009) [1, 3]. AdaBoost is a machine learning algorithm introduced by Freund and Schapire in 1997 [2].

To implementation the FURIA-GA algorithm, the following characteristics were obtained after trial and error. For the GA, crossover probability was set to 0.2, mutation probability was set to 0.035, population size at 250, and the number of generations set to 50. For the FURIA evaluator, a 10-fold cross-validation technique with T-Norm product as a fuzzy aggregation operator was trained to combine rule antecedents. The results showed that the FURIA-GA classification with 86.45% was very accurate. The following settings are used to run the AdaBoost algorithm: batchsize, 100; number of iterations, 12; seed, 1. Decision tree C4.5 was also selected as the base classifier. WEKA software was used to perform these algorithms.

The present study was an attempt to investigate the susceptibility of flood risk in the Aland chai basin. Therefore, 13 effective parameters in flood occurrence were used to prepare a flood risk susceptibility map. ArcGIS and ENVI software were used to prepare each of the information layers. In order to perform the relevant analyzes on each of the parameters, the new ensemble model FURIA-GA-AdaBoost in WEKA software was used. The results of these studies showed that slope, soil hydrological groups, altitude, vegetation, and land use have an important role in the occurrence of floods in the area. Flood risk susceptibility map was prepared in 5 classes of very low, low, medium, high, and very high susceptibility. The results showed that the areas that are highly susceptible in terms of flood risk are mainly concentrated downstream of the basin, which includes flat and low areas. Generally, 26% of the total area of the Aland Chai basin is located in high and very high risk for floods.

In the last few decades, a decreasing trend is observed in livability of cities due to growth of technology and applying the modernity concept in the construction of cities, which causes the adverse effects on urban spaces. One of the most prominent of these effects, which can be called urban damage, is the decline of values associated with the sense of place in the formation of these spaces. This concern is especially highlighted in metropolises where technology has naturally grown faster. This study provides a framework to estimate the livability in Tehran metropolitan district 1 by considering the sense of place factors. In this way, a novel predictive model is developed based on the integration of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA). To this end, several variables based on the sense of place including physical body, meaning, activity, social capital, sense of place, and mental quality of life are considered as predictive variables. Results indicate that the ANFIS-GA model (R=0.926) is the most accurate predictive model when all input parameters are included. Besides, the social capital index has very little effect on livability and the importance of mental quality of life index is more significantly than other indices and can lead to more improvements in livability of regions than other factors.

Moving towards sustainable buildings requires more emphasis on accurate assessment of daylighting and energy performance. This is particularly important for educational buildings, because providing sufficient light while preventing the glare and reducing energy consumption in these spaces is a major challenge. In this article the main purpose is the optimized design of window configuration in terms of daylighting and energy performance in educational buildings (primary school classroom) in Tehran city to provide integrated visual comfort components (increase illuminance and decrease glare) and energy efficiency (reduced overall energy consumption). In the process of achieving this purpose the basic models of the South classroom were modeled parametricly by Grasshopper and dynamic simulation of daylight and energy was performed on them. Then simulation-based automated optimization process through the Genetic algorithm was accomplished in multi-objective.The results indicated that adjustment of the conditions with a higher weight for daylighting performance is necessary. The best Pareto solution, based on the minimum distance to the global optimum performs better than base model, indicating that improvements in UDI, DGP, and EUI purpose are 11, 15, and 22 percent respectively.

 Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ground control pointsnecessarydespitetheexistenceof GPS/IMU data. Therefore, reducing systematic errorsin data naturally increases the accuracy of triangulation and reduces the number of ground control points required forblock adjustment andthe number of crossstrips used to eliminate systematic errorsin GPS data.

 Materials:

Digital images captured by the National Cartographic Centerof Iran from an area in Fars province usingUltraCam-Xpcamera in2010 were used in the present study to investigate the roleof self-calibration parameters in the reduction of ground control points and cross strips requiredfor block adjustmentin aerial triangulation. The intended block consists of 58 images and four strips; two of which are cross strips. Control points in this block include eight horizontal control points, eight vertical control points and eight full control points. Each image has a dimension of 11310 by 17310 pixels, a pixel dimensionof 6 microns, afocal length of 10500 microns, an end lap of 70%, and a side lap of 30%.  Theregion has an average elevation of 760 m. Given the focal length, flight height and pixel dimensions, ground resolution is around 12 centimeters. Each image covers anarea of 2077.2 mlength and 1357.2 mwidth on the ground.

The present study investigates theroleof self-calibration parameters, such as elimination of systematic error in GPS/IMU data and image sensor,in increased accuracy oftriangulation, and reduced number of ground control points and cross strips required for block adjustment. To reach this aim, optimal self-calibration parameters are determined using a genetic algorithm and the identified parameters are used in the bundle block adjustment. Variance components estimation method was used to solve the problem of equationsinstability. This method not only stabilizes the equation, but also determines the optimal weight matrix during the adjustment process.

Since images at a scale of 1:2000 were used in the present study, maximum RMSE equals 60 cm and maximum residual errorsequal 1.2 m. Using additional parameters to eliminate systematic errors results in an acceptable maximum error at the control points, but absence of additional parameters results in an unacceptable maximum error at the horizontal and vertical control points even in the presence of crossstrips.  In addition to the evaluation of horizontal and vertical errors at the ground control points, horizontal and vertical RMSE of the checkpointsare also used to evaluate the geometric accuracy of aerial triangulation. Again, applying additional parameters keeps the RMSE at a much lower level than the accepted limit, while absence of additional parameters results in a horizontal and verticalRMSE higher than the accepted limit even in the presence of cross strips. It should be noted that using cross strips reduces RMSE at the vertical component.

Results indicated that using self-calibration parameters and reducing errorsin data used for the adjustment process decreases the number of control points and cross strips required for block adjustment.Using optimal self-calibration parameters(even in the absence of control points) resultsin a maximum RMSE of 0.143 m at the checkpoints, while absence of these parameters results in a maximum RMSE error of around one meter with or without cross strips. Genetic algorithm is capable of determining optimal self-calibration parameters. It is also capable of optimizing nonlinear functions. Therefore, it is not necessary to linearize the equations before determination of self-calibration parameters, which reduces the amount of necessary calculations. Variance components estimation can also be used along with the bundle block adjustment method to stabilize the equations and determine the optimal weight matrix. As a result, it is suggested to take advantage of these three methods, i.e. block adjustment, stabilization and optimal weight matrixdetermination, simultaneously.

The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher resolution. The results showed that the predicted stream-network landscapes created using the GLE algorithm had the self-similar tree structure of natural stream networks. Also, the results of the genetic algorithm showed that a change in the degree of waterways in the study area over time compared to the current situation, so that the degree of number of first-class waterways in the future will change to grade 3 due to erosion in upper lands. Therefore, using these models, the condition of waterways can be predicted in the future and better management can be adopted for watersheds.

Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries.

Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed:  a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters.

The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).

Nowadays, Determining the effective factors on fire is so important, because the plenty areas of forests around the world are destroyed annually by fire and recurrence of that in the long term can irreparably damage to the earth and its inhabitants. It helps us to identify most dangerous locations and times in forest fire. Hence, we can prevent many of driving factors of forest fire by law enforcement, efficient forest management policies and more supervision. In the current study, we identified the effective factors on the fire in Golestan forest through integration of three different methods including multiple linear regression, logistic regression and multivariate adaptive regression spline with Genetic Algorithm.

Golestan Province is in the North of Iran and 18% of it is covered by forests. Golestan Province is a touristic province and several roads pass through its forests and according to statistical records, most of the occurred fires were in proximity of these roads. Our study area is located in 36°53′-37°25′N and 55°5′- 55°50′E and its area is about 3719.5 km2. We selected this area, because includes the most of fires have been occurred in Golestan Province in recent years.

A big fire was occurred on 12 December, 2010 in our study area and we used it as the dependent variable. The actual burnt area and some other data, such as Digital Elevation Model (DEM), the roads network, the rivers, the land uses, and soil types in the area were provided from Golestan Province Department of Natural Resources. Also, geographic coordination of the synoptic weather stations near the area and their data, including maximum, minimum, and mean temperature; total rainfall, as well as maximum wind speed and azimuth in December 2010 were obtained from National Meteorological Organization of Iran.
The land use and soil layers were in scale of 1:100000 and the roads and the rivers layers were in 1:5000 and all of them were provided in 2006. The region DEM is generated from topographic maps of Iran National Cartographic Center in scale of 1:25000 with positional resolution of 30m and we produced the slope and the aspect layers from it in ArcGIS software with the same resolution. The roads and the rivers were in vector format, hence, we used the Euclidean Distance analysis to generate rasters that each cell of them shows the distance from the nearest road or river.
At first we had 5 weather stations, which is very few for GWR. In this regard, we generated 1000 random points in the area and interpolated data to these points using Ordinary Kriging method with exponential semivariogram model in 30m resolution in ArcGIS software.
The multiple linear regression (MLR) model is the generalization of simple linear regression that is modeling the linear relation between one dependent variable and some independent variables. The general formula of MLR is seen below: (1)The unknown coefficients are obtained using least squares adjustment as follows:  (2)The logistic regression (LR) model is a nonlinear model for determination of the relation between a binary dependent variable and some independent variables. If we use the values of 0 and 1 for non-fire and fire points respectively, then the probability that a point be a fire point is obtained by Eq. (3):   (3)If the number of parameters is insignificant compared to the observations, then we use the unconditional maximum likelihood estimation shown by Eq. (4) to compute the unknown coefficients of this model. (4)The multivariate adaptive regression spline (MARS) model is a flexible non-parametric model that requires no assumption about the relation between the dependent andindependent variables. Hence it has a high ability in determination of complex nonlinear relations among the variables. The general formula of MARS is seen below:   (5) is the m’th basic function that is obtained by Eq. (6):  (6)These basic functions are chosen in such a way that leads to minimum RMSE of model.
We use the genetic algorithem (GA) with the fitness function of the normalized RMSE to select the optimum combination of effective factors on forest fire.

In this paper we study the dependence of the forest fire to 14 factors shown in table 1, in the study area. Our results are shown in figures 1 to 3.
 

This research shows that both of the biophysical and anthropogenic factors have significant effects on forest fire in our study area. Just two factors were identified as impressive factors in all three cases including the minimum temperature and the maximum speed of wind. This study concluded to the NRMSE=0.4291 and R2=0.9862 for the multiple linear regression, NRMSE=0.9416 and R2=0.9912 for the logistic regression and NRMSE=0.1757 and R2=0.9886 for the multivariate adaptive regression spline and totally the multivariate adaptive regression spline method showed a better performance in comparison to the other two methods.