ماشین بردار پشتیبان

Urbanization is a global trend that significantly impacts sustainable urban development and the quality of urban life. Assessing urban sprawl is critical for sustainable urban planning and aligns with the key objectives of the United Nations sustainable development goals. The present descriptive-analytical study aims to identify and measure the intensity of urban sprawl in the city of Al-Diwaniyah, Iraq.

Using geographic technology and landscape indicators, the study assesses, maps, and quantifies the extent of urban sprawl in Al-Diwaniyah from 1990 to 2024. Therefore, to achieve the desired goal, a supervised machine learning algorithm using a support vector machine (SVM) along with landscape criteria was used. In this regard, first, satellite images for the years 1990, 2000, 2011, 2020, and 2024 were extracted using the Landsat satellite and classified and changes were revealed using the SVM algorithm in ENVI4.8 software. Also, by applying landscape metrics using Fragstats 4.2 software, urban sprawl in the city of Al-Diwaniyah was examined and extracted.

The results showed that the amount of construction in Al-Diwaniyah city increased from 2069 hectares in 1990 to 4420 hectares in 2024 during the 34-year study period. In other words, the city witnessed a growth of 113.6%. The study also discovered through landscape criteria that all forms of urban sprawl, leapfrog, infill, strip/ribbon, and low density development characterize the urban sprawl pattern in Diwaniyah.

Floods are one of the natural phenomena that can cause significant damage to infrastructure, farmlands, and the environment. This phenomenon primarily occurs due to heavy rainfall, snowmelt, or a combination of these factors. Therefore, the aim of this study is to map flood hazard zones and examine their relationship with land use using the Analytic Network Process (ANP) model in the Razi Chay watershed in Ardabil Province.

In this study, data from Landsat 8 satellite imagery from 2022, a 30-meter ASTER DEM map, a 1:50,000 scale topographic map, a 1:250,000 scale geological map, and other detailed information of the studied watershed were utilized. Ten parameters influencing flood occurrence were analyzed, including elevation, slope, slope aspect, vegetation cover, geological formations, distance from the river, flow direction, land use, precipitation, and drainage density. The Analytic Network Process (ANP) model was employed to determine the importance of each variable.

Among the studied parameters, slope (with a weight of 30%), elevation (with a weight of 21%), and land use (with a weight of 17%) were assigned the highest weights, indicating their significant influence in controlling flood occurrence in the Razi Chay watershed. The results show that approximately 36% of the Razi Chay watershed falls within high-risk and very high-risk zones. These areas are typically located in the lower part of the watershed, often at the confluence of the two main streams. Considering the spatial distribution of settlements in the region, it can be concluded that most of the settlements in the lower part of the watershed are exposed to flood risks

Potato is the fourth most cultivated crop worldwide. In terms of its strategic role in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation and prediction at any scale. Although remote sensing (RS) approaches based on optical and/or microwave sensors have been widely employed to monitor cultivated lands (including crop area, conditions, and yield forecasting), the identification of potato planting areas using RS data and machine learning has not been much addressed. As a result, the present research addresses the literature gap by suggesting an effective potato mapping approach in Iran's main production center and tries to provide accurate information on the cultivated areas of this crop for the field of agricultural management.

Since most crops have specific spectral and temporal characteristics during their cultivation period, this research has presented a method to discriminate potato fields from other crops using time series images without explicit thresholding. Is. This method identified this product by using layers based on potato phenology and machine learning. We employed the ground truth data of the crop types from the studied site, which included a total of 1648 samples of potato fields and other crops, to optimize the internal parameters of the algorithm, train, and evaluate the model. A handheld GPS receiver was used to collect this data. This research employed Sentinel-2 satellite images and the Support Vector Machine (SVM) algorithm to map potato fields. To accurately identify potato fields, we prepared appropriate input layers, including the phenological index of the potato crop and the median statistical index of NDVI (time series of Sentinel-2 satellite images) at specific intervals. We used these layers as inputs to the SVM. We optimized the gamma and C values using the 5-fold cross-validation method to train the optimal model for SVM using the RBF kernel. We then used these values in the algorithm implementation process under the Google Earth Engine cloud computing platform. We assessed the efficacy of the suggested approach in the Iranian cities of Hamedan and Bahar, key sites for the cultivation of this particular crop.

Based on the results, the optimal values for the internal parameters of the model (C = 70 and γ = 0.3) were calculated. We included these values in the RBF function to identify the cultivated areas of the potato crop. By implementing the classification algorithm and then applying the majority filter, a map of the areas under potato cultivation was prepared for the study area. This map showed the highest density of potato cultivation in the border area of two cities (northwest of Hamedan city and east of Bahar city). The calculated total area for potato farming was 4527.1 hectares in Hamedan city and 6088.3 hectares in Bahar city. The estimated overall accuracy and Kappa coefficient are 90.9% and 0.82 for Hamedan and 93.3% and 0.87 for Bahar, respectively. The present research's results demonstrate the effectiveness of the SVM algorithm in detecting potato cultivation areas, highlighting the potential of using indicators corresponding to potato phenology as distinguishing features for improved identification.

By employing the SVM method, we effectively identified potato fields by utilizing layers of indicators that correspond to crop phenology. At the trial stage, it was demonstrated that this method can improve the potato acreage mapping process. Therefore, a similar approach can be evaluated for identifying other important crops in other regions. It is also suggested that the efficiency of microwave data and other machine learning algorithms be considered in future research.

Avalanche is the fast and downward movement of large masses of snow and avalanches can endanger human lives and cause huge financial losses. Therefore, zoning and evaluating areas prone to avalanches is one of the necessities of environmental planning to prevent crises and The reduction of human and financial losses in different regions. The connection road between Khalkhal and Shahrood sector has attracted special attention due to the development of the communication network in the region and its economic, tourism, and transit importance. This route is located in a mountainous area with special geomorphic and geological conditions, and the occurrence of avalanches in this area has caused many human and financial losses every year. For this reason, the evaluation and zoning of avalanche-prone areas on the Khalkhal Road to the Shahrood section is very important.One of the most basic stages of conducting any research is collecting data and information. In this research, various data and information have been used, including a geological map of Ardabil province with a scale of 1.250000, from which the information about faults was extracted, topographic map of Khalkhal. With a scale of 1/20000, the road map is extracted from this map, the field data of the avalanche points has been collected through the field survey and GPS device, and the important remote sensing data and information include vegetation, land use, elevation map, slope map, slope direction map It was calculated from the images of Sentinel 2 and ALOS-PALSAR satellites. This research is based on field, analytical, and statistical works. Arc GIS, SPSS Modeler, and ENVI software were used to prepare the layers and implement the research model.According to field studies and analysis of satellite images to identify factors affecting avalanches on the road from Khalkhal to Shahrood, 8 layers in order: 1- Height 2- Vegetation 3- Slope direction 4- Distance from the fault 5- Distance from the road 6- Snow area 7- Land use 8- Slope was used with the standard raster format and all the layers were collected together after production and a single raster image was created. After finishing the classification process, modeling was done in SPSS Modeler software with 8 input neurons, 8 intermediate neurons and 1 output, and 70% of the data were allocated for model training and 30% of the data for model testing. The type of algorithm of SVM and MLP model is after error propagation. At the same time, this algorithm is very efficient and makes the model learn as well as possible, and the model process is completed when the minimum amount of error is reached.The results show that in the support vector machine model, the highest weight value for the snow layer is 0.26 and for the slope layer and the distance from the road is 0.18 and 0.15, respectively, which indicates that avalanches and hazards It is more dependent on these variables. Also, in the multi-layer perceptron model, the highest weight value was assigned to the snow area factor with a value of 0.20, followed by the layers of the distance from the road, both values of 0.17 and 0.13.Also the area of risk classes shows that in the support vector machine model, the most class in terms of risk is 36.34 square kilometers and in the multi-layer perceptron model it is 45.83 square kilometers, which shows that the multi-layer perceptron model is better than the volume vector machine model. A large part of the area is classified as high-risk class. Also, in the support vector machine model, the value of 61.57 square kilometers for the high-risk class and 49.32 square kilometers for the multi-layer perceptron model is classified, which shows that there is a big difference between the area values of the models that the support vector machine model had a realistic performance and was able to correctly identify the areas where there was an avalanche process and the risks arising from it, while the multilayer perceptron model did not have a good performance in the high-risk section. In the medium risk section, the results of the gear mark area values are that the support vector machine model with an area of 23.93 and the multilayer perceptron model with a value of 25.83 square kilometers are classified and there is not much difference between the values of the areas, but in the low risk category, the multilayer perceptron model with The area of 15.73 square kilometers is slightly different from the area of the support vector machine model with an area of 14.87 square kilometers.According to the results of two models regarding avalanche risk zoning in Khalkhal axis to Shahroud, also according to the mechanism of avalanche and the risks arising from it, it has been found out by field studies that the morphology of the slopes overlooking the road, the slopes of the central part in the area of Eskistan village due to the angle A slope of 20 to 35% and too close to the sacred road with snow and its accumulation during the winter seasons with the least mobility causes avalanches. The occurrence of avalanches in the region is in the form of slides and masses and does not have a powder state, because the length of the range and also the road factor have a great impact on the occurrence of this avalanche model. The cause of this provocation is known to be the avalanche. The results of the support vector machine model with relatively few deviations from the multi-layer perceptron model have high reliability and have been able to identify dangerous areas well. . Also, the overlap of the identified areas with real points is high in both models, but the results related to the identification of avalanche-prone areas in the support vector machine model have better performance than the multilayer perceptron and can identify the areas realistically and carefully. Therefore, both of these models can be used to identify areas prone to avalanches.

Urban weathering refers to the processes of loosening, decay, and eventual deterioration of materials used in various urban constructions. This study focuses on assessing the degree of weathering in gravestones from the Joy-e-Horhor and Khold-e-Barin cemeteries in Yazd. A combination of petrographic analyses and longitudinal monitoring of Schmidt hammer rebound values for hundreds of gravestones was employed to achieve this. The findings indicate that in addition to the petrographic characteristics of the stones, such as mineralogical composition, mineral diversity, and rock texture and fabric, local climatic conditions significantly influence the weathering and degradation of these materials. Key processes contributing to the loss of stone durability include temperature fluctuations leading to thermal expansion and contraction, the albedo effect of the stone, wet-dry cycling, and the crystallization and dissolution of secondary minerals like calcite and gypsum. Gravestones made of travertine and marble, characterized by a predominance of calcite minerals and light-colored surfaces, exhibit higher resistance to weathering compared to other lithologies, provided they are not exposed to excessive moisture or frequent washing. In contrast, low-grade metamorphic rocks such as slate and phyllite are the least suitable for gravestones due to their high density of fractures and cleavage planes. Similarly, dark-colored igneous rocks are prone to rapid durability loss, as the differential thermal expansion and contraction of their constituent minerals in response to temperature changes accelerate their weathering processes.

The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, 1994; Carranza et al., 2008a). According to the investigations, the common models of map integration that are used in the discovery of mineral reserves in the initial exploration stage include index overlap model, fuzzy operators, weighted indicators and smart methods such as random forests and artificial networks. Determining the values of weights and scores that show the relative importance of the effective factors is the primary requirement in combining the maps and preparing the mineral potential map (Agterberg, 1992; Brown et al., 2000).The purpose of this research is to prepare a potential map of copper deposits in Dehj-Bazman region using two methods of random forest and support vector machine. In addition, in order to compare the potential map of porphyry copper reserves resulting from the random forest method, the support vector machine method and the knowledge-based methods of index overlap and fuzzy logic were used.

The area studied in this research is a part of the magmatic belt of Kerman region, known as the Dehj-Sardouye belt. The information layers controlling mineralization in Dehj-Bazman area include rock units, structures, alterations, geochemistry, geophysics and copper deposits. In practical applications of machine learning algorithms, mineral potential mapping is essentially a bimodal classification problem, such that each undiscovered area is classified as prospective or non-prospective according to some combination of mapping criteria (Zuo, 2011). The final results are a set of predictive maps that show target areas with high ore formation potential.In order to model, training was done. Before training the random forest model, the input data set and the target variable should be prepared and then the model should be trained. The target variables for entering the random forest model and support vector machine were determined as deposit points (values of 1) and non-deposit points (values of 0). Then the genetic algorithm was used to adjust the parameters.Evaluation of the predictive performance of random forest model and support vector machine can be described by the ambiguity matrix. In this matrix, there are four components, which are defined as: (1) a deposit sample that is correctly classified as a deposit (TP); (2) a deposit sample incorrectly classified as a non-deposit sample (FN), (3) a non-deposit sample correctly classified as a non-deposit sample (TN), and (4) a non-deposit sample that is wrongly classified as a deposit sample (FP) (Liu et al., 2005; Tien Bui et al., 2016): (8) (9) (10) (11) (12) After training and evaluating different models, the best model was obtained by adjusting different parameters and it was used to integrate factor maps in order to predict areas with high potential of porphyry copper deposits. Also, knowledge-based methods of fuzzy logic and index overlap were used to combine factor maps to compare with the results of intelligent methods.

At this stage, the desired information layers were collected and prepared in the GIS environment, and then factor maps were prepared. Accuracy, sensitivity, specificity, predicted positive value, predicted negative value, kappa index and OOB error were used to evaluate the performance of random forest model and support vector machine. Also, the importance of the predictor variables in the random forest model was evaluated through the mean decrease in accuracy and the mean decrease in node impurity or the Gini impurity index (Breiman, 2001). According to the results, the most important predictor in the random forest model is the geochemical map, while the structures factor has the least impact in predicting the preparation of the mineral potential map with the final random forest model.In the potential maps of porphyry copper deposits obtained from two methods of random forest and support vector machine, the target areas cover 14% of the studied area, in which there are 92% and 87% of known deposits, respectively. Finally, the efficiency of machine learning methods and knowledge-based methods were compared. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. For this purpose, weights of 0.3, 0.25, 0.25, 0.1, 0.1 were assigned to produce maps of alteration factor, geochemistry, geology, geophysics and structures respectively. In the potential map obtained from the method of index overlap and fuzzy logic (fuzzy sum), the areas predicted as copper mines cover 16 and 17 percent of the studied area, respectively, in which 83 and 79 percent of the existing mines are located.

This research was conducted with the aim of evaluating and comparing the effectiveness of random forest method and support vector machine method and knowledge-based methods to prepare porphyry copper potential map of Dehaj-Bozman region of Kerman province. Based on the results, the random forest model works well in the field of porphyry copper potential map preparation with geochemical, geophysical, geological, alteration and structures datasets. In addition, the random forest algorithm can estimate the importance of factor maps.The results of this research show that the geochemical factor map is the most important and the structure factor map is the least important in predicting the data-driven model of random forests. This estimate of importance is consistent with geological knowledge about porphyry copper mineralization in Dehj-Buzman region. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. According to the obtained results, the performance of the random forest model is better than the vector machine model, and also, the performance of the support vector machine model is better than the knowledge-based methods.

One of the key priorities of the Ministry of Agriculture Jihad is the mapping of croplands to estimate crop acreage and annual yield. In recent decades, remote sensing technology has proven to be highly effective in estimating the extent of crop cultivation through the use of timely images and synchronized data with diverse spatial, temporal, and spectral resolutions, leveraging advanced machine-learning algorithms. This study presented a framework for crop mapping in Marvdasht, Fars Province, by utilizing time series of Landsat-8 satellite images and advanced machine-learning algorithms. The employed algorithms included Decision Tree (DT), Random Forest (RF), Rotation Forest (RoF), Support Vector Machine (SVM), and Dynamic Time Warping (DTW) analysis. The results indicated that the dynamic time warping and random forest methods outperformed others, achieving significantly higher accuracy (with an overall accuracy improvement of 10-12%) in generating the agricultural land-use map of the study area. Furthermore, this research demonstrated the effectiveness of Bands 2-5 of Landsat-8 satellite in confidently identifying all crops in this region using the mentioned methods.

The purpose of this research is to evaluate the amount of soil loss in the land uses of the Nirchai watershed using the RUSLE model in Ardabil province. In order to carry out this research, first, the satellite image of the studied area related to the year 1400 and the month of June was received from the American Geological Research Center, and after atmospheric and radiometric corrections, a land use map was prepared using the supervised classification method using the support vector machine method. Then the RUSLE model was used to estimate the erosion rate. SPSS 21, Excel, ArcGIS 5.4, Archydro and ENVI 5.3 software were used to analyze and produce maps in this research. RUSLE model parameter layer includes rain erosion layer, soil layer, topography layer, vegetation layer and soil protection factor as well as various statistics related to rain gauge stations, hydrometry, topographic maps 1:50000, geology 1:100000 as well as DEM (20 meters area) and GIS geographic information system and remote sensing have been used. The results of this study showed that the average amount of annual soil erosion for the whole basin ranges from 0.5 to 14.25 tons per hectare per year. Also, the investigation of the regression relationships between the factors of RUSLE model and the amount of annual soil erosion showed that the topography factor (LS) with the highest value of the coefficient of determination R^2=0.93 is the most important in estimating the annual soil erosion using the RUSLE model.

With the development of various methods in the field of classification of satellite images and the detection of changes, especially in recent decades, the selection of the best and most correct method for preparing land use and land cover maps in different regions has grown day by day. The main goal of this research is to compare the accuracy of various classification methods for preparing land use maps in Razvanshahr city. For this purpose, seven land use classes in Razvanshahr city (including pastures, residential areas, roads, agricultural lands, rivers, coastal areas and forests) were determined using satellite images. Then, educational samples were collected from the region using satellite images, Google Earth images and field visits. In the next step, by using the characteristics of the images, the land use classes in the study area were determined and after determining the resolution of the classes, the classification was done using maximum likelihood (MLC), support vector machine (SVM), and minimum mean distance (MD). The evaluation results of these three methods showed that the machine vector method has more accuracy than other methods (overall accuracy 92.60, kappa coefficient 0.87 for the year 2000 and overall accuracy 97.16 and kappa coefficient 0.93 for 2022). . Therefore, the results of this research can be used to prepare a land use map with higher accuracy by using the support vector method in environmental and natural resource assessment works in areas with similar conditions.

One of the important conditions for optimal use of land is obtaining information about landuse patterns and their changes over time. Landuse is usually defined based on human use of land, emphasizing the role of land in economic activities. Today, remote sensing technology is considered as the main element in landuse monitoring. The aim of the current research is to extract landuse maps for the years 2000 and 2021 in FirozabadKhalkhal region and to investigate the changes made in the studied time period in the region using the images of ETM and OLI sensors of Landsat. Also, checking the capability of basic pixel and object-oriented methods for landuse classification is another purpose of this study. In the current research, the object-oriented technique nearest neighbor algorithm and the vector machine method supporting the pixel-based algorithm have been used for landuse classification. Then, to verify the accuracy of these two methods, the overall accuracy and Kappa were extracted. The results of this evaluation show the high accuracy of the object-oriented method in extracting land use classes. Based on the results of the detection of landuse changes in the studied time period, the highest amount of changes occurred is related to the use of good pasture to poor pasture with a value of 51.72 square kilometers, followed by forest to good pasture with a value of 30.11 and the lowest changes It is related to the use of pasture and water with the amount of 0.03 square kilometers. The reasons for these changes are the increase in population, indiscriminate grazing of livestock, incorrect and illegal use of different lands. The use of more parameters such as scale, shape, compactness, color, texture, smoothness criterion and pattern for landuse classification in the object oriented technique can be considered as an innovation of the present study.

The aim of this study is to develop sensitive gully erosion models by implementing a machine learning algorithm (Support Vector Machine and Boosted Regression Tree) in the Moher basin. First, gully areas are identified, and then 13 variables predisposing to gully erosion (Slope, Slope Direction, Topographic Wetness Index, Streem Power Index, Terrain Ruggedness Index, Distance from Waterway, Drainage Density, Distance from Road, Land use, NDVI, Avera annual Rainfall, Geology, and Soil Texture) were selected. The variance inflation coefficient was used to evaluate multicollinearity between variables. Finally, a gully erosion sensitivity map was prepared in the environment (R). Also, the effect of physical and chemical characteristics of soil on gully erosion was investigated using Multivariate Regression. Regarding the importance of variables, Geology has the most significant effect on gully erosion in the SVM model, Land use, and the BRT model. The predicted sensitivity map was validated with the help of the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) in the Support Vector Machine and Boosted Regression Tree models were calculated as 0.92 and 0.94, respectively, which led to accurate prediction. Also, the results showed that the sand variable (9.299), sodium absorption ratio (7.967), and TNV (6.185) have the most significant effect on gully erosion

One of the basic solutions to control and reduce the destructive effects of floods is to identify flood prone areas in the regions. Identifying flood sensitive points is one of the best methods for planning and identifying areas affected by floods. For this reason, determining flood-sensitive areas plays an important role in flood management in natural resources. For this reason, the current research is trying to determine the flood-prone areas in Isfahan province by using two methods of random forest machine learning and support vector machine and 3327 flood occurrence points. Environmental factors in four main groups including topographical factors (altitude, slope direction, steepness of slope), climatic factors (rainfall, relative humidity, wind, temperature), biological factors (vegetation and soil moisture) and man-made factors (distance from areas residential, distance from road, distance from agricultural land, distance from waterway) were prepared. The accuracy of the used models was evaluated using the area under the graph (AUC) and cross evaluation statistics. Examining the AUC index showed that both models had good accuracy, although the random forest model (AUC = 0.97) had higher accuracy than the support vector machine model (AUC = 0.86). According to the results of the random forest model, about 41% are in the high risk class and about 20% are in the low flood risk class. Also, in the support vector machine model, about 29% is in the high risk class and about 30% is in the low risk class.

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.

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.

Humans can affect the regional natural and biological environment by changing land use. In order to detect such changes in the earth's surface features, remote sensing is a practical methods. Surface features are detected and then analyzed in the studied area using the chronological charactirstics of the satellite data.
Methodology Various digital methods have been developed to show changes in land cover and the earth surface transformations using remote sensing. Landsat 7 satellite images taken in December 2000 and those of Worldview2 taken in December 2020 were used over a 20-year period to compare object-based (Support Vector Machine) and pixel-based (Maximum Likelihood) methods. The mentioned satellite images were used to investigate land use changes trend in the western region of Tehran.

The spatial resolution of the images and the algorithm used to extract features of the earth's surface were more efficient in world View 2 images. The overall accuracy and kappa coefficient for Landsat 7 images were 56.9767% and 0.3962, respectively, while for Worldview2, they were 86.0465% and 0.8069, respectively. Over the 20-year period, bare lands area decreased by 9.61 square kilometers, vegetation-covered area decreased by 5.13 square kilometers, constructed areas increased by 13.13 square kilometers, and water areas increased by approximately one square kilometer due to the construction of Chitgar Lake.

The results of the evaluations indicate that the support vector machine method is more compatible than the maximum likelihood method to detect land use types and land cover characteristics, as it incorporates additional factors beyond spectral reflection, such as shape, size, and texture factors between bands.

Soil erosion is a global problem that threatens water and soil resources and land use change is one of the important factors in soil erosion intensification. The aim of this study was to evaluate the effect of land use change on soil erosion in Razeychay watershed of Meshginshahr located in Ardabil province. First, Landsat images of the study area in May 1999, and 2019 and were obtained from USGS website. In the image processing stage, atmospheric and radiometric corrections have been conducted, and then the land use maps of the study area has been prepared for study years using support vector machine (SVM) as a supervised classification method. Then, the RUSLE model was used to estimate the amount of erosion in the two time span. SPSS, Excel, Arc GIS 5.4, Archydro and ENVI 5.3 software were used to spatial analysis and data processing.The results showed that, rangeland, irrigated farming and bare lands have decreased during the last twenty years. While, the extent of dry farming and residential area have increased. Meanwhile, the highest change is related to dry farming (an increase of 27.69 hectares). According to the results of erosion modeling, the rate of erosion from 1999 to 2019 has decreased from 6.49 to 6.46 tons per hectare per year.

The type and intensity of soil erosion in a region depends on climatic conditions, lowland and elevation of the land, soil and land use, among which the importance of land use is greater than other factors due to the effective role of humans on it. The purpose of this research is to evaluate and analyze the amount of soil erosion in the Balikhlochai watershed using the RUSLE experimental model in Ardabil province. In order to carry out this research, first, the satellite image of the studied area from the year 1400 and the month of June was obtained from the US Geological Survey Center, and after atmospheric and radiometric corrections, a land use map was prepared using the supervised classification method using support vector machine. Then the RUSLE model was used to estimate the erosion rate. SPSS 21, Excel, ArcGIS 5.4, Archydro and ENVI 5.3 software were used to analyze and produce maps in this research. RUSLE model parameter layer including rain erosion layer, soil layer, topography layer, vegetation layer and soil protection factor as well as various statistics related to rain gauge stations, hydrometry, topographic maps 1:50000, geology 1:100000 as well as DEM (20 meters area) and GIS geographic information system and remote sensing have been used. The results of this study showed that the average amount of annual soil erosion for the whole basin ranges from -6.65 to 14.75 tons per hectare per year. Also, the investigation of regression relationships between the factors of RUSLE model and the amount of annual soil erosion showed that the topography factor (LS) with the highest coefficient of determination R^2=0.95 is the most important in the estimation of annual soil erosion using the RUSLE model.

In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.

In recent years, maritime and aerial surveillance have become commonplace for marine pollution control; however, these methods alone cannot provide rapid and systematic monitoring due to the limitations of weather conditions, time, and location. In this regard, satellite remote sensing can play an important role in the initial detection and continuous monitoring of oil spills at sea. The synthetic aperture radar (SAR) sensor is an active microwave sensing system that can be used for oil spill detection, along with optical sensors such as MODIS, with simultaneous imaging capability. The aim of this study was to detect the oil spills around oil platforms in the northern part of the Persian Gulf on June 15, and 17, 2015, using MODIS thermal infrared imagery and Sentinel-1 images. To estimate the sea surface temperature, the split-window algorithm was applied to band 20 of MODIS. Results showed that the sea surface covered by oil spill has lower temperature than surroundings. For accurate detection of oil slicks and accuracy assessment of the results of applied image processing method on the MODIS data, the Sentinel-1 vertical polarization image and noise removal processes such as filtering and multi-looking were used. Finally, by comparing the field temperature measured by Boushehr marine waveguide and the temperature estimated for the MODIS image, and review of the geographical location of detected oil slicks, the accuracy of the results of this study and the applied image processing methods were confirmed. Application of MODIS band 20 aiming the extraction of sea-surface temperature, and its thermal infrared bands for oil spill detection at sea surface are evaluated in this study for the first time.

groundwater forms part of the water cycle and is a reliable source of water for human consumption, as well as in Iran, most of the water used in the drinking, agricultural and industrial sectors is supplied from groundwater. Due to the condition of Iran, due to the deficit surface water resources, the use of groundwater resources for water supply has been considered. 

1) Use of the Radial basis function of the neural network
If a generalized regression function of the neural network, PNN / GRNN, is selected, all of the network weights can be calculated as probable. In RBF, a Gaussian transmission function is used which is similar to a ring (GRNN) One of the benefits of these networks is its rapid learning of other networks, including the multi-layered perceptron network of MLPs. The Gaussian networks of the transfer function network are of an unidentified learning type, but the output is a controlled learning type. The network is very practical in simulating hydrological and hydrological issues, due to its rapid training, generalizability and ease of use.
 
2) Use a support vector machine
  A support vector machine is proposed based on the principle of minimizing structural error. A support vector machine can be used both for categorization issues and for the estimation of functions. used a new error function called ε-insensitive for machine application in regression problems, so that this function ignores errors that are at a given distance from actual values. This function is defined as How to design a network.
In this study, the data used were 95 piezo metric wells in the Amol-Babol Plain. Data were used with a mean average of 30 years. In order to simulate the depth of the groundwater table, effective factors such transmissivity of aquifer formations, precipitation values and distance from water resources. For the design of the network, for both models, there are two classes of training and testing data. One important criterion for training a network is the number of repetitions or epoch during training. The higher the number of replays, the error decreases so that training data can be converted, which will increase the number of unsuccessful repetitions at that time. for network optimization purposes, the goal of network training is to reduce network error, which can improve the relationship between the input and output of the model. Due to the lack of specific rules for designing artificial neural networks (ANNs), various structures have been investigated to optimize the design. Select the number and type of input parameters for the model is important. For this reason, seven design input patterns are given. Which was carried out in the software of the NeuroSolutions. 

for the optimal simulation model based on all parameters and the provision of all its input data will require a great deal of time and cost, a method based on the main parameters of input (optimal inputs) is modeled and validated. it was observed that the predicted aquifer level for both models is about to its actual value.

The results obtained with the Radial basis function and the support vector machine represent this point where the support vector machine and the radial function have the ability to have approximately the same ability to predict and modeling, although generally the results of the Radial basis function are more acceptable. The results of the model test are shown. As the results of the survey are presented, among the methods implemented in the model, using effective factors such as transmissivity of aquifer formations, precipitation values and distance from water resources to predict level of level The aquifer was used. The results of the test showed that the Radial basis function of the support vector machine with a correlation coefficient of 0.82 and a mean absolute error of 1.94 is an appropriate tool for prediction of water resource management.