artificial neural network

One of the hazards that threatens the existing infrastructures in different regions is the phenomenon of landslides. The present study tries to identify the prone areas of this natural phenomenon in Khalkhal city, which was done using the neural network method. For this purpose, 9 factors affecting landslides were identified and prepared, the layer of landslides that happened, obtained from aerial photos and satellite images and field visits, and using non-sliding points in the region, perceptron neural network training data. They created several layers. In order to model the neural network, these data were transferred to the MATLAB 2016 software after initial preparation in the ARC GIS 10.5 software environment and were trained using MLP neural network coding to deal with the data that they have not encountered. , make predictions. The structure of the designed neural network was selected from among the many networks that were created and tested, 1-12-9, which obtained 9 inputs as the number of effective criteria, 12 neurons in the middle layer and one neuron and layer for the output of the network. The results of the validation chart of the neural network (ROC) model show a high accuracy of 95% of the created model in predicting sliding pixels. According to the results, 0.57%, 0.11%, 0.07%, 0.06% and 0.07% of the studied area were placed in very high, high, medium, low and very low classes, respectively. Due to the fact that the largest area of the region is in a very high risk class,

In this study, using images of Landsat-8, Landsat-7 and Sentinel-2 satellites in the coding environment of Google Earth Engine, their uses and changes during the two periods before and after urbanization (from 2000 to 2008 and from 2008 to 2019) will be categorized and then the next five-year development forecast of Sahand city (until 2025) will be made. Perceptron multilayer artificial neural network (MLP) method has been used as a method for predicting spatial multi-criteria decision making (MCDM). The independent variables used in the present study in predicting the physical development of the city are land price, type of use, slope, slope direction, altitude, distance from urban areas, distance from waterway network, distance from fault, distance from network Passages (main and secondary). The results of classification of satellite images showed that the physical development of Sahand new city has been done in order to turn barren lands into urban land. In addition, physical development was built to turn cheaper land into areas. The built lands have been greatly developed and from 64,155 square meters in 2000 to 682,192 square meters in 2019. Among the image classification methods for land use extraction, the SVM method was the best method and also the Sentinel-2 satellite images had the highest accuracy. The multilayer perceptron artificial neural network was used to predict the future physical development of the new city of Sahand, which according to studies, the development is predicted in directions that are based on the cheapness of the land and the limitations. Geomorphological is like slope and altitude.

The most important pillar of a scientific and applied research is the statement of the problem, when a problem can be scientific and practical that creates a challenge in relation to the solution of the problem and clearly defines the purpose of the work, as well as the challenges that have arisen in relation to the problem in question, the researcher uses Simulation models can overcome one of the challenges and work as a source of information for climate researchers to use for future research. In this research, the statement of the present problem is the statement of forecasting the average seasonal temperature. What element is temperature, the answer to these questions and reasons, let's hypothesize against it, after formulating the assumptions, prepare climatic data of temperature of the study area and the neighboring stations of the area, and also specify the study area To start the work, using modeling (simulation) and comparison and accuracy of forecasting, he used two models by comparing and measuring the accuracy of their errors, because Temperature is a physical quantity, some of the sun's radiant energy is absorbed by the earth's surface and becomes thermal energy.This energy is expressed in the form of temperature or degrees. Among the different climatic elements, temperature and precipitation are of special importance to predict this. The important key climatic element, our goal is to examine the seasonal average temperature changes in the seasons and determine the seasonal changes with 95% and 80% accuracy using artificial neural network - Arima time series model, RMSE index, and also the models together Let's compare which predicts temperature changes better. So that researchers can use and test these models in future researches to predict other climate parameters and also the impactful consequences of seasonal temperature changes and climate elements such as relative humidity - evaporation and transpiration - industry - transportation - bridges and other infrastructures. Proper planning and management should be done in this regard. In the 21st century, climate change is considered one of the biggest environmental threats to the world. Changes in Farin's climate are estimated to have more negative effects on human society and the natural environment than changes in the average climate (Mahmood and Babel, 2014: 56). Based on the fourth report of the International Commission on Climate Change, which was published under the title of Climate Change Assessment Reports, the global increase in temperature and the occurrence of climate change have been confirmed by using the measured data of the surface temperature of land and water in the world (IPC Si, 2014: 32). The first effect of climate change on atmospheric elements is especially temperature and precipitation, then due to the relationship between atmospheric elements and terrestrial ecosystems, water resources, vegetation, soil and also human life will be affected by this phenomenon; Therefore, investigating the trend of atmospheric variables such as temperature is of particular importance (Abkar et al., 2013: 14).Temperature Some of the radiant energy of the sun absorbed by the effects of the earth's surface turns into thermal energy. This energy is manifested in the form of temperature or degree. Among the different climatic elements, temperature and precipitation are of particular importance. Although the main cause of temperature is the energy obtained from the absorption of short solar radiation on the earth's surface.Using artificial neural network and Arima time seriesThe purpose of this research is to model forecasting changes in seasonal average temperature in the study area of Al-Shatar city using artificial neural network and Arima time series model and to determine the measurement accuracy of neural network models and Arima time series model in forecasting average temperature changes and also The above simulation models should be used to predict the research of future climate researchers and be realized.The main goals of this research are to model and identify seasonal average temperature changes and the relationship of this key element with other climatic parameters of Al-Shatar city. In terms of seasonal average temperature changes and prioritizing areas with temperature variability.This part of the research has monitored and simulated the regression error of Lorestan stations (Alshatar-Broujerd-Aligodarz-Noorabad-Khorramabad-Poldakhter) in the time period (1998-2018) of the stations of Lorestan province with the temporal-spatial analysis of the RMSE index. The obtained results show that the indicators of the cold period of the year in the current situation in different areas (stations of Lorestan province) have had different trends, but the average temperature of the cold seasons of autumn and winter is an increasing trend, which results in the melting of the glaciers and snowfall. Rain is coming and this process is predicted for the next eleven (11) years. In general, the results obtained in this section have shown that the heat waves in the future will be more intense, sharper and more lasting than the current situation, and the highest temperature fluctuations in the autumn season, which is 81. Using RMSE = .003 and ME = .86, it is the artificial neural network that has the best efficiency and performance in Elshatar city station and predicts the average temperature better than the Arima time series model, therefore the artificial neural network model and Arima time series Both have 95% and 80% measurement accuracy. It is better to use these models and other machines in future research to predict the minimum and maximum temperature and other climatic elements. Because they have the best performance and efficiency in forecasting the elements, forecasting the average seasonal temperature can help to plan and manage, control evaporation and transpiration and other resources of the country and Al-Shatar city. It is summer and the least accuracy is in autumn and winter. Considering that the prevailing rain-producing air masses in Al-Shatar city leave the most seasonal changes in autumn and winter, it can be concluded that the most temperature fluctuations occur in the cold seasons of the year and the least fluctuations in The summer season occurs due to the deactivation of the rain-producing western wind, whose value is 33/. Is

Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly temperature.
The most commonly used tool for simulating the responses of the climate to greenhouse gas increases and examining future temperature changes is the use of climate variables simulated by coupled atmosphere-ocean models (AOGCMs). General circulation models (GCMs) are powerful tools aimed at generating climate scenarios. However, GCMs cannot provide effective information on climate simulation at local and regional scales. Therefore, the downscaling method is used to bridge the gap between local and global scales.
The current research aims to simulate maximum temperature using an artificial neural network model that adopts data from the atmospheric general circulation model (HadCM3) under RCP8.5, RCP4.5, and RCP2.6 scenarios for the Yazd synoptic station from 2006 to 2095. The independent variable, as the input to the artificial neural network, was selected for statistical downscaling using four statistical criteria: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. Finally, the maximum temperature of the Yazd synoptic station for the next century was simulated.

The present study aims to investigate the maximum temperature of Yazd's synoptic station in the context of climate change based on valid scenarios until 2095. To achieve this, three sets of data were used: average daily maximum temperature data from Yazd's synoptic station, observed atmospheric data for the period of 1961 to 2005 (NCEP data), and simulated data from 2006 to 2095 based on release RCP scenarios. The NCEP data from 1961 to 2005 included 26 atmospheric variables that will be used as independent or predictor variables.
Modeling, simulating, and forecasting temperature based on nonlinear and chaotic time series is a challenging task. Prior studies have shown that artificial neural networks (ANNs) are suitable for simulating and predicting basic processes that are not well known. It is crucial to select the correct input variables intelligently and according to the purpose of the artificial neural network's design for prediction and simulation. Accordingly, in this study, the most suitable atmospheric parameters as the input of the artificial neural network were selected by pre-processing and selecting the atmospheric variables for the base period (1961-2005) to simulate with four statistical criteria (Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry). The resulting mean square error (MSE) obtained from the statistical criteria was compared, and the correlation coefficient and the similarity of the monthly time series trend of the simulated values with the target values were also analyzed. The best network architecture was selected to simulate the maximum temperature of Yazd's synoptic station from 2006 to 2095 under different RCP emission scenarios.

The selection of explanatory variables for downscaling was based on four statistical methods Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. After analyzing the mean square error (MSE), correlation coefficient, monthly average values of the maximum temperature of Yazd station, and estimated values from 1961 to 2005, the probability density function, cumulative probability function, and monthly time series trend obtained from all four methods, the explanatory variables were selected. These variables include mean sea level pressure, the divergence of 1000 hPa, zonal wind component, zonal wind intensity of 850 and 500 hPa, altitude and vorticity of 500 hPa, average temperature, and relative humidity at a 2 m height.
The structure and architecture of the neural network were designed based on these selected variables. The network consisted of a two-layer feedforward, with a sigmoid transfer function in the hidden layer, a linear function in the output layer, an input layer with eight variables, eight neurons, and the Lunberg-Marquardt training algorithm. This architecture was used to simulate the maximum temperature of Yazd's synoptic station under RCP2.6, RCP4.5, and RCP8.5 scenarios for two periods of 2050-2006 and 2095-2051.
Comparing the monthly average values of RCPs (RCP2.6, RCP4.5, and RCP8.5) in the first statistical period (2050-2006) with the base period (1961-2005), the maximum temperature of Yazd station indicates an increase in temperature in winter, spring, and summer, and a decrease in the autumn season under all three RCPs.
Comparing the monthly mean values of RCPs (RCP2.6, RCP4.5, and RCP8.5) of the second period (2051-1995) with measured mean maximum temperature (2005-1961) showed that temperature will increase the most in winter, spring, and summer, similar to the first period of the RCP8.5 scenario. In this scenario, unlike the other scenarios, the increase in temperature is evident in both subperiods for the autumn season. Finally, in the second period (2051-1995), the increase in the average maximum temperature of Yazd station in winter, spring, and summer, and the decrease in the average maximum temperature in autumn will be more significant.

The increase in greenhouse gas concentration resulting from human industrial activities is expected to cause global and regional warming in the future. The current study's findings indicate that the average maximum temperature of Yazd station will rise between 0.4 to 6.9  in winter, 0.2 to 8.1  in spring, and 1.1 to 7.7 in summer from 2006 to 2095. However, a decrease in the maximum temperature between 0.6 and 1.4  is expected in autumn. These results are consistent with those of other researchers.

Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of  the most important factors (David and Nicholas, 1994). Sand areas are one of the desert  landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding  and  interaction with the environment and ultimately its sustainable management  and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.

The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.

In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.

Sediment that consists of solid particles with organic matter transported by water is called suspended sediment. In other words, the sediment load is the flow of the total sediment output from the catchment or drainage basin that can be measured in the desired cross-section and in a certain period of time. In most natural rivers, most of the sediments are transported as suspended load. The sediments collected by the rivers cause many problems, including sedimentation in the reservoirs of dams and reducing their useful volume, changing the course of the river due to sedimentation in their bed, reducing the capacity of canals and water transfer facilities, and changing the quality of water in terms of drinking and agriculture. . However, no direct or indirect experimental model developed to evaluate this process has been universally accepted. Therefore, sediment transport has been considered by engineers from different aspects and different methods have been used to estimate it. One of the methods of estimating suspended sediment is artificial neural network. Artificial neural network is a computing mechanism that is able to provide a series of new information by taking information and calculating it. Considering that the structure of the human brain has a very high ability to process complex, non-linear and parallel information.

As one of the sub-basins of the Aras catchment basin, Gharasu catchment is located in the geographical coordinates of 47°31' to 48°47' east longitude and 37°47' to 38°52' north latitude.In this study, the statistics and information of 17 variables in 13 sub-basins of the Gharasu River, which were extracted by the regional water organization of Ardabil province, were obtained from this organization. In order to model the artificial neural network from the data of 3834 daily sediments recorded in 13 sediment measuring stations in the studied sub-basins during a 50-year statistical period corresponding to the statistical period of 1350 to 1399 and also from the digital topographic maps of the 1:25000 scale of the Geographical Organization of the Armed Forces to Validation of basin demarcation was used. In choosing this common time base, criteria of completeness, sufficient length of data and use of the latest available data were taken into consideration. Then the normality and correlation between the obtained data were evaluated and two methods of Radius Axis Function (RBF) and Multilayer Perceptron (MLP) were used in SPSS software to model the artificial neural network.

recorded suspended sediment (3834 cases) in the relevant statistical period was considered as a dependent variable and flow rate as an independent variable separately for each sub-basin, and Pearson's correlation method was used to check the correlation between the independent variable and the dependent variable.According to the correlation matrix of the variables, it can be seen that Barouk sub-basin has the highest correlation and Arrab Kendi and Pol Almas sub-basins have the lowest correlation. After modeling the data by two artificial neural network models (RBF and MLP), the amount of sediment for each year was predicted by these models and R2 and RMSE values were also calculated for them. In order to determine the number of neurons in the hidden layer, the values of the neurons in this layer were evaluated by trial and error, and according to the results, choosing the number of 4 neurons for the RBF model and 3 neurons for the MLP model has the highest prediction accuracy in the evaluation data and It also shows in the test data. The accuracy of prediction in RBF model with correlation coefficient R2=0.941 and RMSE=65.002 is compared to MLP model with R2=0.917 and RMSE=88.244.Based on the scatter diagram between the real data and the estimated data, it was determined that the average of the real values is 4.636, which is 4.367 for the RBF model and 3.534 for the LMP model, which indicates better accuracy in Modeling and the closeness of the RBF model value to the real value. Regarding the median index and the mode index, which represent the most repeated data in the statistical collection, for the real values, the numbers are 4.117 and 3.246, respectively, and for the RBF model, the numbers are 4.425 and 4.213, respectively, which are the closest values. It is considered as a real amount.

So far, various forecasting models have been used to estimate river sedimentation. Some of these models have estimated the amount of sediment by combining different physical parameters of the basin, climate and even the output of satellite images. Artificial neural network models are widely used in forecasting geographic models today.In this research, two artificial neural network models, radial axis function (RBF) and multi-layer perceptron (MLP) in SPSS software have been used to estimate the sediment of Gharasu River in Ardabil province. In this study, recorded suspended sediment (3834 cases) in a 50-year statistical period was considered as a dependent variable and flow rate as an independent variable separately for each sub-basin, and Pearson's correlation method was used to check the correlation between the independent variable and the dependent variable. It was found that Barouk sub-basin had the highest correlation and Arbab Kendi and Pol Almas sub-basins had the lowest correlation. After modeling the data by artificial neural network model, the amount of sediment for each year was predicted by these models and R2 and RMSE values were also calculated for them. The prediction accuracy of RBF model with correlation coefficient R2=0.941 and RMSE=65.002 is higher than MLP model with R2=0.917 and RMSE=88.244, and it has a better performance in estimating suspended sediment in the study basin.Also, the average value of the real values is equal to 4.636, which is equal to 4.367 for the RBF model. This research showed that in all studied stations, the RBF method provides more accurate estimates of suspended sediment than the MLP model. Of course, due to the existence of complex relationships between flow rate and suspended sediment, the appropriate model should be determined in each hydrometric station to estimate this variable more accurately,

The city of Tabriz, which has the fastest urban growth in the northwest of the country, is one of the largest cities in Iran in terms of population, economic activity, industry and transportation options. Public transportation and industry combustion and lack of proper filtration of these industries, such as thermal power plants, has led to increased air pollution in the city. For this purpose, the present study tries to use input variables (distance from industrial centers, humidity, temperature, population density, distance from commercial centers, distance from bus stations, distance from educational centers, vegetation changes, distance from free Roads, building density, wind direction, carbon dioxide and carbon monoxide) to assess air pollution using artificial neural networks in the metropolis of Tabriz. In the present study, the independent variables affecting the distribution of pollution probability in two models of multilayer perceptron neural network (MLP) and linear regression were tried to be defined by defining measures in urban management and influencing and planning the mentioned variables. Improve pollution control.The results show that the major pollutants are mostly suspended particles (PM10), gas (CO2), (SO2) and (NOx).The dispersion of airborne particles is mostly due to vehicle traffic, industrial activities, fuel combustion of diesel engines and construction and the need to generate more electricity.-The activities of thermal power plants, Tabriz refinery and domestic and commercial heating systems are also among the factors producing SO2 and the highest CO2 production is related to the fuel of gasoline-burning vehicles. The intensity of the increase in the amount of this pollutant in all selected stations in the autumn and winter seasons is much higher, so that in these seasons the pollutants reach more than twice the allowable level.The share of Tabriz air pollutants can be divided into three general categories, the most important of which is the thermal power plant and transportation.

Severe climate changes (and global warming) in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. The process of climate change, especially temperature changes, is one of the most important challenges in the field of earth sciences and environmental sciences. Any change in the temperature characteristics, as one of the important climatic elements of any region, causes a change in the climatic structure of that region. The summary of the investigated experimental models on climate change shows that if the concentration of greenhouse gases increases in the same way, the average temperature of the earth will increase dangerously in the near future. More than 70% of the world's CO2 emissions are attributed to cities. It is expected that with the continuation of the urbanization process, the amount of greenhouse gases will increase. According to the fifth report of the International Panel on Climate Change, the average global temperature has increased by 0.85 degrees Celsius during 1880-2012. Therefore, knowing the temperature changes and trends in environmental planning based on the climate knowledge of each point and region seems essential. For this reason, the present study simulates the daily temperature (minimum, maximum and average) of Zanjan until the year 2100.

The method of conducting the research is descriptive-analytical and the method of collecting data is library (documents). To check the temperature of Zanjan city, the minimum, maximum and average daily temperature data from Hamdeed station of Zanjan city during the period of 1961-2021 were used. The data of general atmospheric circulation model was used to simulate climate variables (minimum, average and maximum temperature) using artificial neural network and climate scenarios in future periods. The output variables in this study are minimum, maximum and average daily temperature. Therefore, three neural network models were selected. For model simulation, model inputs (independent variables) need to be selected from among 26 atmospheric variables. Therefore, two methods of progressive and step-by-step elimination were chosen to determine the inputs of the model. In these methods, climate variables that have the highest correlation with minimum, maximum and average daily temperature were selected. By using RCP2.6, RCP4.5 and RCP8.5 scenarios, variables were simulated until the year 2100. Markov chain model was used to check the possibility of occurrence of extreme temperatures of the simulated values.

According to the RCP2.6, RCP4.5 and RCP8.5 scenarios and the simulation made by the neural network model, it is possible that on average the minimum temperature will be 3.6 degrees Celsius, the average temperature will be 3.3 degrees Celsius and the maximum temperature will be 2.7 degrees Celsius. Celsius will rise. The monthly review of the simulated data for all scenarios and the observed data of the studied variables shows that the average minimum, average and maximum temperatures in January and February, which are the coldest months of the year, will increase the most and become warmer. While the average minimum temperature in August, the average temperature in April and the maximum temperature in October will have the least increase. According to the simulated seasonal temperature table based on all scenarios, it was found that the average minimum, average and maximum temperature observed with the maximum simulated conditions were 6.9, 5.5 and 5.4 respectively in the winter season, and 3.3 in the spring season. 4, 2.3 and 3, in the summer season it increases by 3.3, 3.4 and 1.4 and in the autumn season it increases by 4.6, 4.5 and zero degrees. The frequency of extreme temperatures observed in all three variables of minimum, average and maximum temperature for the 25th and 75th quartiles is less than the number of occurrences of extreme temperatures simulated in all three scenarios. Based on this, all three variables will increase and there will be fewer cold periods. An increase in night temperature and average temperature in winter season and maximum temperature in summer season will occur more than other seasons. The difference between day and night temperature will be less in autumn and summer. Also, all seasons, especially the summer season, will be hotter and the occurrence of extreme temperatures is increasing for the coming years.

 Iran is a vast country with a different climate due to its geographical location and topographical conditions. The current research was carried out to select the most appropriate drought index in Iran's climatic regions and investigate the probability of its occurrence through uncertainty methods. 

 In this research, in the first step, through multi-criteria decision-making methods, the most suitable index for each climate zone is selected based on the percentage of suitability, and finally, based on artificial neural network methods, probability analysis is calculated and the probability of the phenomenon occurring. In this research, after choosing the appropriate index, the statistical data of the country's synoptic station in a statistical period of 28 years (1990-2017) has been used to express the probability of drought, and the Kernel method has been used to converge the data.

 The final result of the cloud theory analysis of the studied data shows that in all the stations examined in the target year, i.e., 2017, the country of Iran and all the representative stations have shown climatic conditions close to the normal range. The highest likelihood of occurrence belongs to Tabriz station (96%) and Hamedan station (94%).

 Based on the results, the selective uncertainty model has a high ability in probability analysis and has predicted the probability of drought with an acceptable percentage of confidence.

Innovation: 

Due to the difference in climatic regions of Iran, the elimination of user intervention, and the use of scientific and mathematical calculations, the error rate in selecting the index is reduced. Then, with the help of uncertainty methods such as cloud theory, the ability to predict the probability of drought in the future increases.

Today, due to the high value of land and the limitation of natural resources in the city of Bowanat, it is very important to predict land use changes in this city.

Research Aime: 

determining the level of ability in modeling the localization phenomena in the city of Bowanat is one of the main goals of the research.

Due to practicality and development in this research, artificial neural networks were used to calibrate the model for effective factors in the city of Bowanat, and ENVI and Arc GIS image processing software were used.

Studied Areas:

 Bowanat city is located 240 km from Shiraz city with an area of 4992.2 square kilometers, which is located at 30.46 degrees north and 53.67 degrees east.

In the design of urban growth modeling in Bowanat between 2003 and 2018 using artificial neural network, it was observed that for two main reasons, the mentioned model is suitable for predicting land use changes in Bowanat city, the first reason being the ability of the CA model and the reason The second is to achieve a model for urban change and expansion by changing urban land use.

After examining the findings, it was found that the road network is one of the most important factors in the growth and expansion of Bowanat city, and in addition, the percentage of land slope is one of the effective parameters in the modeling of Bowanat city.Keywords: Land use, Fuzzy Logic, Artificial neural network, Bavanat city.

The simulation of the rainfall-runoff process is a crucial step in water resources management, watershed management, water scarcity crisis, and flood control. The intrinsic complexity of the rainfall-runoff process, spatiotemporal variability, and the factors affecting it make the simulation with physical or hydrological models difficult. Therefore, metaheuristic approaches, such as support vector machines, gene expression programming, and artificial neural networks, have been widely used in hydrological studies, and generally, in the phenomena without definite relationships. Due to the provision of drinking, agricultural, and industrial water, the drainage basin of Aharchay, located in the northwest of Iran, has an influential role in the development of the region. This paper has evaluated the models of support vector machine, gene expression programming, and artificial neural networks for the simulation of the rainfall-runoff process in the drainage basin of Aharchay at the hydrometric stations of Tazeh Kand, Ravasjan, Oushdilaq, Barmis, Owrang, and Kasin. In order to determine the input combination of the models, a list of independent variables associated with the runoff of each station was prepared. Then the appropriate inputs were chosen using the two criteria of the Pearson correlation coefficient and partial mutual information. The input combinations obtained from each criterion were evaluated in the simulation of the rainfall-runoff of the Aharchay drainage basin in the hydrometer stations. The results indicated the reasonable accuracy of the models of support vector machine and gene expression programming, and the relative superiority of the artificial neural network. Moreover, in determining the input variables, the Pearson correlation coefficient provided the best results or was close to them.

Wetlands are vital for human survival and constitute 40.6% of the total value of global ecosystem services. This study analyzed land cover changes in the Golpayegan Shoor wetland from 1988 to 2018 and then modeled it with a combination of artificial neural network, Markov chain, and multi-objective optimization for 2030 and 2040 for obtaining environmental sustainability. To obtain the highest accuracy in transition potential modeling, four scenarios were used with various calibration periods and sub-models. The FOM index was used to evaluate the accuracy of various scenarios using the approach by overlaying three maps for the years 2008, actual 2018, and predicted 2018 maps. Effective variables in describing the changes occurred in the case study selected using Cramer's v. The highest accuracy was shown in a scenario with the calibration period of 1988-1998 and 9 sub-models in land cover change modeling 2018 year. The evidence likelihood of change map and distance from agricultural lands showed the highest impact in the changes of the study area. Residential areas and agricultural lands increased by 0.6% and 1.8% in 2030, while 1% and 2.2% grew up in 2040 compared to 2018, respectively. Dense rangeland decreased by 13.1% and 18.6%, respectively in 2030 and 2040 compared to 2018. The water bodies showed an increase of 0.18% and 0.27% due to the conversion of agricultural areas and rangeland to Lake Golpayegan and Kochery dams. Taking water from unauthorized wells, reducing the flow of water entering the wetland, and the presence of decorative ore has caused serious problems for Golpayegan Shoor Wetland, which requires managers and land planners to consider compensatory ways to restore, water requirement, and ecological sustainability for the wetland.

   The diverse ecosystems in Iran with their own unique climate and wildlife have witnessed uncontrolled fires annually to the extent that in terms of forest fires, Iran ranks fourth among MENA countries (Naghipoor Borj, 2018). In 2017, a total of 252 incidents of wildfire occurred in Iran, with damage to 3,006 hectares; while in 2018, 187 wildfires occurred damaging 2,385 hectares (Sabzali et al., 2019). The Zagros forests cover an expanse of 6 million km2 in the West of Iran (approximately %4 of Iran’s total land mass) (Sadeghi et al., 2017) of which 33,920.2 km2 are located in the Khuzestan province (Alli Mahmoodi Sarab et al., 2013). The Shimbar Mountains are chiefly composed of limestone formations, and only a small area is composed of alluvial deposits. The average annual rainfall in the area is roughly 815 mm, and the average annual temperature is 20-26°C. The average evaporation rate for the area is 2,523 mm. (Sharifi et al., 2020). The vegetation cover of the Shimbar natural reserve is composed of two types of vegetation: the marshland vegetation cover which is chiefly Artemisia as the main vegetation cover, and the mountain vegetation cover which is a type of Iranian oak.
Due to the high security of the wildlife reserve, a variety of mammals thrive in this region such as the Iranian squirrel, martens, wolves, wild bear, and the mongoose. Birds such as quail, partridge, bee-eaters and woodpeckers are also native to the area (Dinarvand et al., 2018).
 Shimbar region was decreed by national legislature to be among the four areas under the jurisdiction of the National Environmental Protection Agency (NEPA). In the early 1940s, the first attempts to apply a logic-based model to simulate fire hazard risks was carried out by Warren McCulloch and Walter Pitts, and this logic model is the basis of all present day artificial neural networks (Laurent Fast, 2016). In the present study, the underlying reason for the selecting of an artificial neural network was its capability in the creating a relationship between the input and output data for non-linear complex phenomena, its extensive application in the prediction of fire hazards, and its ability to create a model out of the relationship between the number of fires and the factors impelling such fires (Ouache et al., 2021 & Islami et al., 2020 & Polinova et al., 2019 & Jaafari Goldarq et al., 2013).

Initially, the data related to forest fires that had occurred in the period spanning 2011 to 2018 were collected from the Andika regional environmental protection agency, and in the next stage, the ground reality maps of these points were prepared. The environmental protection agency had recorded 79 fires in the wildlife preserve; therefore, the data was used for the training and testing of the model used in this study. In order to prepare a map identifying potential fire hazard zones, the factors affecting the forest fires in the region were identified as chiefly being physiological features such as slope, aspect, and elevation. All topographic maps with a scale of 1:25,000 were obtained from the Andika regional environmental protection agency, and the National cartographic center. Features of the vegetation cover such as soil types, land classification types, vegetation cover, and land use were obtained from LANDSAT 8 images and the complementary data were obtained from the Natural resource's organization of Andika. Climate features such as relative humidity, wind speed, minimum temperature, maximum temperature, wind direction, amount of rainfall, and average temperature were accessed from the archives of the regional meteorological office. With due regards to the fact that the data values obtained were at various points, and in order to generate data for the whole region, the interpolation functions in ArcGIS were utilized. Anthropological features considered were far from residential areas, road accessibility, and distance from the river. The data for road accessibility were obtained from Google Earth layer maps and the data for residential areas and distance to rivers were provided by the Andika regional environmental protection agency and Natural resources offices. The Information layers for roads, rivers and residential areas used a vector format; therefore, by using Euclidean distance analysis, Raster-geomatics with the capability of spatial segregation for the required zones were developed in a way that the value allocated to each cell indicated the distance from the nearest road, the river or residential area. Once the features for each of the variables were identified, a spatial map was prepared in a GIS environment.

The data used in this study encompass historical data of forest fires occurring from 2011 to 2018. By analyzing the maps created for various parameters such as the forest fire dispersion map, physiological features map, vegetation cover map, meteorological map, anthropological specification map, the results showed that the vegetation cover; the distance to the available bodies of water, and the type of lands are the main factors to be considered. The validation of the model was assessed by utilizing RMSE- ROC-AUC criteria in order to verify the accuracy of the obtained results for determining the extent of potential forest fires with actual events. It was observed that the method proposed in this research has an accuracy of 0.83 in predicting fluctuations along the trend which is relatively high. The data were then transferred to the Arc GIS software and the zoning map for determining the potential fire hazard areas based on the existing historical data was created and divided into five categories representing very low risk, low risk, moderate risk, high risk, and extremely high-risk fire hazard potential. The results/maps also indicated the percentage of fire hazard classifications based on the artificial neural network. It was observed that %31 of the areas are classified as extremely low risk; %28 are classified as low risk; %20 are defined as moderate risk; %11 are classified as high risk; and the remainder %10 are classified as extremely high risk zones.

The artificial neural network, much like its counterpart in the human body, is independent of the distribution of the data, and is capable of adequately evaluating the problems besetting natural resources. Comparative studies carried out by Ngoc et al. (2018) and Sachdeva et al. (2018) showed that the artificial neural network can provide the best evaluation for determining the potential fire hazards in a region.
The current study has found that the recent forest fires have taken a severe toll on the Southern Zagros forests in the Shimbar wildlife preservation. By determining the effective factors influencing the risk of forest fires and incorporating these factors into an artificial neural network, a zoning map for the pinpointing of areas with the greatest risk of fire hazard has been created. The zones were classified into five categories, ranging from extremely low to extremely high-risk fire hazard regions. The validity of the model was evaluated as being 0.83 and the RMSE= 0.75, which in itself is indicative of the accuracy and efficiency of the artificial neural network method in developing a map for determining potential fire hazard areas. The obtained results were in parallel with the findings of a study conducted by Vidamanesh et al. (2018) who used artificial neural networks with a validity of 0.88 to determine the zones in the Mazandaran Forest and grasslands where there is a greater risk of forest fires. It also corroborated the findings of studies conducted by Anderson et al. (2021) on predicting the forest fires using artificial neural networks which had a validity of 0.81, and Elia et al. (2020) whose model had a validity of 0.82.
 The graph for the importance of independent and dependent factors showed that in the artificial neural network the factors for scorched and unburnt areas (dependent variable), distance from the body of water, type of land classification, elevation and minimum temperature (independent variable) in sequence had the most important effect, and the direction of the slope had the least important impact on the risk of fire hazard in the region. Moreover, it was observed that %41 of the area under study was classified as being moderate risk, high risk and extremely high-risk zones. The results obtained paralleled those in the study of Eslamiee et al (2021) conducted on the forests in the Babolrud region in which by applying an artificial neuron network, it was seen that %35 of the region under study was classified as having a high to extremely high risk of fire hazard, and the most important variables affecting the occurrence of forest fire were defined as being temperature, rainfall, and distance from a rural center. The results of the current research were also in line with the results obtained by Zheng et al. (2019) who used an artificial neural network with an AUC = 0.86 to model the sensitivity of forested areas in China to fire hazards. The study stated that the most important variables affecting the occurrence of a forest fire are temperature, wind, rainfall and elevation.
Based on the results of the current study, the forested areas which are composed of oak, juniper, wild pistachio, almond, mesquites and tamarisk have a distance from the bodies of water and the vegetation cover in the region under study ranges from a thick canopy to an average canopy. The rural centers and roads among the forested area have a medium to low population density and are chiefly located in the northern and central areas of the region under study. The populated area is mostly located at elevations ranging from average to low asl, the region has a minimal slope, the temperature fluctuates from 50° C to 1.8° C (average temperature is 25.6° C) and low rainfall occurs.

Simultaneous use of Geographic Information System (GIS) and methods based on artificial intelligence has always led to good results in research in the field of natural resources. This study was conducted in the same format and in order to prioritize the factors affecting the spread of fire and identify high risk areas in the forests of the Shimbar protected area, in this regard, factors for the artificial neural network method were considered. In implementing the artificial neural network method with factors affecting forest fire, a fire risk zoning map with five classes of very low risk, low risk, medium risk, high risk, very high risk with a total accuracy of 0.83 and RMSE error is prepared. It is 0.75. The results showed that 20% of the area in the middle class, 11% of the area in the upper class and 10% of the area in the very upper class, the potential for fire, also the most important variables affecting the occurrence of fire including distance from the river, type Lands, altitude and minimum temperature. It is concluded that according to the considered factors, the integrated models of artificial neural network (ANN) and spatial information system have a high efficiency in preparing a fire risk zoning map and it is suggested that these models be used to prevent, control and manage fires elsewhere. The country should also be used on a large scale.

Environmental sustainability assessment is one of the most important tools in the process of sustainable development planning and therefore attention to it in policy-making and planning is inevitable. The purpose of this paper is to evaluate the environmental sustainability in urban areas with the approach of the dust problem using multi-criteria decision-making technique of linear allocation and artificial neural network in Ahvaz metropolis. In this research, first, by studying the theoretical foundations of the research, the criteria that are effective for environmental sustainability were identified. Then, the documentary and library method and referring to different departments and organizations were used to collect information in the theoretical part. However, the main information of the research was collected using field studies (completing questionnaires, observations and field studies). The research process was such that a questionnaire was designed to implement the methodology through the criteria obtained from the study of the problem literature and distributed among the people in 8 regions by stratified random sampling method. The sample size of the total areas was determined based on the unlimited Cochran's formula in the form of population classes of the areas. Keywords: Environmental stability, particulate matter, linear allocation, artificial neural network, Ahvaz

One of the most important problems of the urban network in Iran is the rapid expansion of cities and, consequently, the uneven development and growth of cities, which has occurred for various reasons, including increasing population and irregular migration. The first consequence of urban sprawl is land use change. The city of Bojnord, after being selected as the political-administrative center of North Khorasan province in 2005, faced a double population growth and demographic changes. The concentration of administrative, military, tourist, cultural and educational centers, along with the uncontrolled migration of villagers to the city, confronted the city with new urban development needs. The physical expansion of the city more than ever, the conversion of agricultural land into a city and the uncontrolled construction, is one of the main issues facing the city. In this regard, the use of cellular automation, as a technique with features such as simplicity, transparency and strong potential to simulate spatial dynamics, has caused more and more attention in modeling the spatial information system and urban affairs

The present study was performed by descriptive-analytical method and using cellular automation method by perceptron artificial neural networks and remote sensing data. Thus, satellite images related to the years 2011 and 2021 of Bojnord plain were the basis of the study and in order to image the spatial-temporal patterns of land use, the data of the remote sensing archive were used. Research data from satellite images related to 2011 and 2021 (a period of ten years) as well as topographic maps of the plain and the city of Bojnord along with some spatial data of the region including land price, distance from land uses, distance from fault, slope, Height, distance from the road and distance from the built areas are provided. The source and organization of this data is also related to the Surveying Organization, Google Earth and the Surveying Organization of Iran. In line with the objectives of the research, the multispectral images of Landsat 7 and Landsat 8 in relation to the city of Bojnord (path 161, row 034) have been downloaded. In order to increase the validity of the results, the output maps were adapted to Google Earth maps. Also, in the 2011 classification, the overall accuracy was 86 and the kappa coefficient was 81, and in 2021, the overall accuracy was 88 and the kappa coefficient was 85.

The main findings include calculating and estimating land use changes and the share of each land use in the base years of this research, modeling and forecasting land use changes for 2031 based on mapping the potential for transfers for land uses and forming a Markov chain along with eight affecting factors: slope, height, Distance from faults, agricultural lands, main roads, main electricity network, built-up areas and land prices. In 2011, the largest area of the region was occupied by pastures, which accounted for 73.6% of the total land area. During this period, the built-up areas cover 4.2% of the area with an area of 53748901 square meters. In general, most of the pastures are in the southern and eastern regions of the area and the areas built in the central and northern part of the Bojnord plain, as well as agricultural lands are located in the central and eastern parts. According to the data obtained in 2021, the built-up areas include 65266522 square meters, agriculture 194519833 square meters, pastures 888136607 square meters and barren 130533045 square meters. The share of rangelands in land use of Bojnord plain is 69.4%, barren 10.3%, agriculture 15.2% and developed areas 5.1%. Decreasing pasture and agricultural levels and increasing built and barren levels are evident this year. In modeling land use change, the transfer force from one land use to another is modeled according to the variables; In the sense that each pixel of the image has the potential to change from one user to another. According to the calculation maps, it can be seen that the changes in the built areas in the central part of Bojnord plain and around the city of Bojnord have been mainly accompanied by changes in agricultural lands and pastures. In all parts, especially in the northwestern areas of Bojnord plain, rangelands have become agricultural lands and with the development of agriculture, these valuable lands, which are the habitats of animals and plants of different species, have been destroyed; Also, Bojnord-Esfarayen axis has become the most built areas.

This study to model and predict land use change in Bojnord, based on the two years based on 2011 and 2021 AD, divided land use into four main categories: constructed use, barren, agricultural and rangeland. The main findings with emphasis on the effect of eight factors affecting the slope, height, distance from the fault, agricultural lands, main roads, main electricity network, built-up areas and land prices, on the development of Bojnord based on data received from satellite images and the process of image correction to increase the validity and accuracy of research outputs shows that while the rangeland use level between 2011 and 2021 from 73.6% of total land uses decreased to 69.4% and agricultural land use level from 17.4%  decreased to 15.2%,  built-in (urban) user level increased from 4.2% to 5.1%. This increase has often occurred due to the greater impact of slope, height, distance from main roads and distance from built-up areas, and the greatest proportion of this change has occurred in the axis of Bojnord Esfarayen road (west and southwest of the city). Based on the formation of the Markov chain matrix and the created user class maps in 2011 and 2021, land use forecasting and modeling of Bojnord in the horizon of 2031 has been done.The results of the land use change forecast section confirm the increase of built-up areas and urban use and the decrease of agricultural and rangeland use in the horizon of Bojnord city development forecast in 2031.