فهرست مطالب فرزانه وکیلی تجره

Identification of the areas prone to landslides and the risks arising from them is one of the primary measures in natural resource management and development and construction planning. Considering the loss of lives, financial losses and environmental effects, landslides have been one of the most important natural disasters in the world and especially in our country, which every year plays an increasing role in the destruction of communication roads, pastures, gardens, residential areas, cause erosion and produces a high volume of sediment in the watersheds of the country. These issues have led to the use of data mining models in geological and geotechnical studies. In recent years, the use of geographic information systems and remote sensing along with machine learning methods has created a new step in landslide occurrence zoning and landslide susceptibility maps with appropriate accuracy. The watershed of Karaj Dam is one of the areas prone to landslides due to mountainous and rainy conditions and many construction due to suitable weather conditions and non-standard road construction. The purpose of this research is to prioritize the factors affecting landslides using the maximum entropy model (MaxEnt model) and to determine areas with landslide susceptibility potential.

The Karaj Dam watershed is located in the east of Alborz Province. The highest and lowest average annual rainfall is calculated as 1099 and 608 mm, respectively. In this research, in order to determine the areas with landslide susceptibility, among 11 factors affecting the landslide potential of the area, including height, slope, slope direction, distance from waterway, lithology, rainfall, land use, topographic moisture index, surface curvature, distance from the waterway and the distance from the road, the factors were selected and tested for collinearity with the Variance Inflation Factor (VIF) test in SPSS software. From the total of 477 landslides, 70% were randomly classified as test data (334 points) and 30% as validation data (143 points). In this research, the maximum entropy model is used. To determine the most important parameters, the jackknife diagram and the Relative Performance Detection Curve (ROC) were used to determine the predictive power of the model. Landslide points of the studied area were prepared from the database of the General Directorate of Natural Resources and Watershed Management of Alborz Province and field visits.

The results showed that there is no co-linearity between the used factors. According to the Jackknife diagram, rainfall layers, distance from the road, lithology and land use were respectively the most important factors influencing the occurrence of landslides in the study area. The relative performance detection curve showed the accuracy of 90% (excellent) of the maximum entropy method in the training phase and 83% (very good) in the validation phase. According to the final landslide susceptibility map, more than 35% of the study area has high and very high landslide susceptibility potential.

According to the obtained results, it can be said that the MaxEnt model has a high ability to determine landslide-susceptible areas, and due to the high speed and accuracy of the model, it is suggested that it be used in similar research, especially in developing countries. The reason for the lack of facilities and financial resources, as well as the time-consuming nature of identifying landslide sensitive areas, should be used. In addition to natural factors, some human factors such as road construction play an important role in the occurrence of landslides, and in order to reduce the relative risks, it is necessary to avoid changing the ecosystem as a driver of natural disasters. In general, it can be stated that the watershed of Karaj Dam has a high potential for landslide susceptibility, that most of the susceptible areas are located near roads, and because there are many human interventions in these areas. Landslide sensitivity has increased. It is suggested to combine geographic information systems with maximum entropy method in order to determine areas with landslide susceptibility, especially in developing countries like Iran, where access to landslide information and data is limited by time and cost. The results of this research can be used in decision-making and preparation of provincial land as well as urban planning and will play a significant role in preventing and reducing damage caused by landslides.

The aim of this study was to model the landslide susceptibility using the Random Forest Machine learning technique and prioritization of effective factors on landslide occurrence in Bar watershed in Khorasan Razavi province. The random forest algorithm is based on a bunch of decision trees and is currently one of the best machine learning algorithms. For this purpose, a landslide inventory map was created with 73 historical landslides, which was randomly divided into two datasets for model training (70%) and model testing (30%). A total of 16 landslide-conditioning factors were considered for the susceptibility landslide mapping. The random forest algorithm was run and a landslide susceptibility map was prepared. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. The results of evaluation indicated that the success and prediction rates of the model were 90% in training and 89% in validation, respectively. These results confirm the ability of random forest method for prediction of landslide susceptibility models. Also, prioritization of the effective factors showed that the slope length and slope had the highest effect on landslide occurrence. Based on the results of the random forest model, 23.7% of the study area is located in a very high and high sensitivity zone.

The present research aims to determine areas with flood susceptibility using CART, RF and BRT models. Twelve factors affecting flood susceptibility including altitude )DEM), slope, aspect, distance from stream, lithology, rainfall, land use, SPI, TPI, TWI, curvature and RSP were selected. Out of 82 flood points, 70 percent to 30 percent were randomly classified as training and validation data. Also, random forest method was used to determine the most important parameters. The ROC curve was also used to validation the model. According to the random forest model, DEM, distance from stream, rainfall, land use and RSP were the most important factors affecting the susceptibility and probability of floods, respectively. According to the ROC chart, the accuracy of the RF model as a superior model has been very good in both training )0.884) and validation )0.856). According to the final flood susceptibility map, 32.7 percent of the study area has a medium to high flood susceptibility. The results showed due to the high accuracy of the spatial distribution map of flood susceptibility can be promising for decision makers, local managers and policymakers to reduce flood damages.

Gully erosion is a water erosion that has a great contribution to land degradation and is known as one of the most important environmental hazards in the world and especially in Iran. In recent years, machine learning techniques and geographic information systems have been highly effective in determining areas sensitive to gully erosion and have increased accuracy and speed in the evaluation and potential of gully erosion and in determining effective factors on gully erosion has also been effective. The loess lands of Golestan Province are more susceptible to water erosion due to sufficient depth and almost uniform silty graining, excessive use, cultivation on sloping lands, and wrong land management so that all types of erosion can be observed in these areas. The most common type of erosion in these sediments is gully erosion. The studied watershed is faced with the increase of dry and abandoned land, land use change, the presence of surplus livestock in the forests, and also the population increase. Therefore, this area is facing an increase in sensitivity to gully erosion, and areas with the potential for gully erosion should be identified and managed. 

The studied watershed with an area of 222,000 ha and an elevation range of 58 to 2168 m is located in the northeast of Golestan Province. The average rainfall of the area is between 224 and 736 mm. In this research, first, the location of the gullies was obtained from the General Directorate of Natural Resources and Watershed Management of Golestan Province. Then, from the total of 1127 gullies position, 70% were randomly classified as training data and 30% as validation data. To determine the effective variables in gully erosion sensitivity, 14 factors were identified and in the next step, the collinearity test between the variables was performed using SPSS software. By using the indices of tolerance coefficient and variance inflation factor, if there is collinearity between the variables, they were removed from the modeling process. Considering the importance of the DEM map and its application in the preparation of various factors of the current research, a DEM was prepared using ALOS satellite images. The layers of slope and aspect are prepared by using a digital elevation model and slope and aspect functions respectively. Slope length index in SAGA GIS software, layers of distance from stream based on the map of stream, and distance from roads based on existing roads, and using the Euclidean distance function in the ArcGIS software was prepared. Stream density and road density layers were obtained based on the map of existing streams and roads in the region and using the line density function in ArcGIS. The lithology layer was extracted from the geological map of the region and the land use layer was obtained from the General Directorate of Natural Resources and Watershed Management of Golestan province. The rainfall map has been prepared using the information from 35 rain gauge stations. First, the average rainfall of 26 years was calculated for each station, and then rainfall zoning was done using the global Kriging Method (due to the lowest RMSE) in ArcGIS. The TPI layer was prepared using the DEM and using the SAGA GIS software. The HAND index is a topographic-hydrological index of the DEM of the nearest drain, representing the hydrological behavior of the watershed. To evaluate the models, the relative performance detection curve (ROC) was used for the predictive power of the models.  

The results showed that there is no co-linearity between the variables and therefore all the variables were used in the modeling process. The relationship between gully erosion and elevation showed that lower elevations are more sensitive than higher elevations and more susceptible to gully erosion near waterways. The results showed that with the increase in drainage density, the sensitivity of gully erosion increases, and the possibility of gully erosion increases. The results showed that the old barracks, shale, and loess have the greatest impact on the sensitivity of gully erosion. The results show a decrease in the sensitivity of gully erosion with a decrease in the HAND index. This result indicates that in the areas where the level of saturation in the watershed level increases, the possibility and sensitivity of gully erosion increases. The results showed that among the types of land use, canals, poor pastures, and agricultural land use have the highest sensitivity to gully erosion. This is even though the forest areas have the lowest sensitivity to this erosion. The results showed that in the rainfall range of 220 to 420 mm, the possibility of gully erosion has increased, and the range of 420 to 500 mm has shown the highest level of sensitivity, and with the increase of rainfall from 500 mm to above, a reduction in the sensitivity of gully erosion has been encountered. One of the reasons for reducing the sensitivity of gully erosion in higher rainfalls is the increase in vegetation and the creation of suitable conditions for landslides. The results showed that the depth of the valley up to 235 meters have increased the probability of gully erosion, and from 235 meters above, it has decreased the probability of erosion. The results showed that the sensitivity of gully erosion increases near roads, and this case shows the effects of road construction and the aggravation of conditions for gully erosion. 

This research was conducted to determine the effective factors on gully erosion and zone its spatial distribution in the northeast of Golestan Province. In this study, by considering 14 important factors and using RF, ANN, and CART models, a sensitivity map of gully erosion was prepared. Because the identification of gully erosion-sensitive areas based on traditional methods and expert opinions do not have acceptable accuracy, it is necessary to use modern machine learning methods. The results showed that the factors of distance from the road and land use are the most important factors affecting the sensitivity of gully erosion, which requires land use management as human activities. The ROC curve showed that the accuracy of the models in estimating areas with gully erosion sensitivity was excellent in the test stage (ANN) and very good in the test and validation stage (RF and CART), which means the excellent performance of the models.

Karaj dam basin in Alborz province is prone to destructive floods given to its special topographic and anthropogenic conditions. The purpose of this study is to determine the areas with flood susceptibility potential and prioritize the factors affecting it using the maximum entropy method. 14 factors affecting flood potential selection and linear testing Factors were analyzed by variance inflation factor. Out of a total of 47 flood points, 70 percent were randomly classified as training data and 30 percent as validation data. The Jackknife test was also used to determine the important factors. The ROC curve was used to predict the model. The results showed that there is no alignment between the factors used and therefore all factors were used in the modeling process. The results showed that according to the Jackknife test, DEM, distance from the stream and rainfall were the most important factors affecting the susceptibility of floods, respectively. Based on the area under the curve and ROC diagram, the accuracy of the model prediction was higher in the higher training sector (0.925) and good in the accreditation sector (0.717). According to the final flood susceptibility map, 22.41 percent of the study area has a moderate to high flood susceptibility potential.

In the present research, changes in sediment regime in two statistical periods of "natural flow" and "modified / debris flow" were evaluated. In order to determine the significant difference between two mentioned periods (1966-1992) and (1993-2015), the significance test was used. In order to determine the most suitable method of Sediment Rating Curve and correction coefficients, the results of simple linear, bilinear, midline rating curve methods as well as the mentioned rating curves with correction coefficients including correction coefficients of FAO, MVUE, QMLE and Smearing methods were evaluated. The results showed that since 1993, with the onset of large floods, the sediment discharge regime with a high intensity and increasing trend is out of normal and in the period 2011 to 2015, the highest rate of sediment transfer in the form of debris flow has occurred. Among the methods, the one-line curve method and the smearing correction factor for the natural sediment regime period (1966-1992) and the two-line curve method and the smearing correction coefficient for the modified sediment regime / debris flow period (1993-2015) are more accurate in estimating sediment flow. On the other hand, the one-line curve method without taking into account the correction coefficients have a higher low estimation error and among the mentioned correction coefficients, the FAO coefficient has a very high overestimation error. In the batch middle method, sediment estimation at high discharges is improved by reducing the effect of low points.

Dust storms, one of the environmental problems in Central Iran, annually damage natural and human ecosystems. The dust has irreversible effects on human societies, including respiratory diseases and declining public health. Therefore, performing research on this subject and identifying the origin of dust is of great importance. Simulation and numerical analysis of severe dust storms are one of the new methods of studying this phenomenon. For the numerical analysis of dust, data related to the storm of July 10, 2014, and the MACC-ECMWF model were used for simulation. The results of the MACC-ECMWF model were extracted based on the Aerosol Optical Depth indices and dust concentration. The time series of the changes in the value of the AOD indices and dust concentration in a one-hour period indicated that the values ​​of these two indices had an increasing trend and reached a maximum at 23:00. Also, spatial analysis of the Aerosol Optical Depth and dust concentrations showed that the Central Desert of Iran, extending from Semnan and East Isfahan Provinces to the Central Loot Desert in the east of the Province of Kerman, is the largest dust source in Central Iran.

The aim of this study was to determine the areas sensitive to gully erosion and prioritize the factors affecting it in Kermanshah province, using the maximum entropy method (MaxEnt model). 9 factors affecting gully erosion potential in the area Including Elevation, Slope, Slope Direction, Lithology, Land Use, Topographic Moisture Index, Surface Curvature, Drainage Density and RSP was reviewed. Of the total gullies studied in the study, 70 percent were randomly classified as test data and 30 percent as validation data. To determine the most important parameters, the Jackknife diagram and the relative performance detection curve ROC were used to predict the model. The results showed that there is no alignment between the factors used. According to the Jackknife diagram, the lithological layers, slope, height and  drainage density were the most important factors influencing the susceptibility of gully erosion, respectively. ROC curve showed 91percent accuracy of the maximum entropy method in the training phase and 72 percent in the validation phase. According to the moat susceptibility zoning map, about 23.39 percent of the area has low sensitivity, 48.14 percent very low, 9.31percent high and 4.71percent very high and 14.46 percent has moderate sensitivity.

Landslides are one of the most destructive phenomena of nature that cause damage to both property and life every year, therefore, landslide susceptibility zonation is necessary for planning future developmental activities. In this study, the operator of fuzzy logic in landslide hazard zonation has been used in ArcGIS software environment in the southern and northern slopes of Binalood mountain range in Khorasan Razavi province. For this purpose, first layers of 20 factors related to the occurrence of landslide phenomenon were prepared. After fuzzyizing the layers in the ArcGIS environment, the landslide risk zoning was performed using fuzzy Product, fuzzy sum, gamma 0.8 and 0.9. Finally, the accuracy of the obtained maps was evaluated using ROC curve and landslide distribution map. Evaluation of landslide zoning results showed that Fuuzy product and fuzzy Sum operators due to their high and very low sensitivity are not suitable for landslide risk zoning. But fuzzy gamma-ray operation is suitable for landslide zonation. According to the results of the ROC curve and the landslide distribution map, the gamma operators (0.8 and 0.9) have the best performance in modeling and zoning the landslide risk. According to the Landslide Risk Zoning Map using Gamma 0.8, about 72.4 percent of the area has low and very low sensitivity, 9.8 percent has high and very high sensitivity and 17.8 percent has moderate sensitivity.

One of the important challenges and issues in land management programs is that of physical growth of cities in different directions over time. The transformation of non-urban watersheds into urban ones has led to some environmental challenges and problems. An overall management plan needs accurate spatial information at consecutive times. Remote sensing technology offers great help in identifying and detecting land use changes. The aim of this study was to detect land use changes using images collected from 1991 and 2001 Landsat 5 satellite¢s TM sensor and 2018 images provided by the Landsat 8 satellite¢s OLI sensor over a period of 27 years on Abshuran Watershed, the Kermanshah. Following the required procedures in the preprocessing stage, a change detection map was prepared with an acceptable accuracy with supervised classification in the ENVI5.3 software environment. The results indicated that during the 2001-2018 periods, about 1.17 % of the share of the rangeland and farmland decreased and that the urban and residential lands increased from 5.92 % to 17.19 % of the total area. Most of the land use change has occurred in the urban and residential areas, thus an increase in the impermeable areas causes an increase in natural disasters such as flooding in addition to some negative effects on natural resources.

Zoning of gully erosion susceptibility and determining the factors controlling gully erosion is very important and vital. The aim of this study was to investigate the spatial distribution of gully erosion using two models of ANN and MaxEnt and to determine the factors affecting this type of erosion in Golestan Dam basin. Therefore, 14 factors in the form of three divisions, including topographic factors, other factors and combination of factors (14 factors) were considered as predictors of sensitivity. Out of 1042 gully erosion points, 30 and 70 percent were randomly classified as validation and test data, respectively. The results of Jackknife test showed that the parameters of height, rainfall and depth of valley are the most important variables affecting the prediction of gully erosion. The results of the modeling showed that the best accuracy of the model based on the ROC curve in the training model (0.923) and in the validation, stage (0.902) was the artificial neural network model, and this condition is achieved when all the factors in the modeling be involved. According to this model, about more than 20 percent of the domain (45633 ha) has a high sensitivity and is very susceptible to gully erosion.

Physical development of cities and expansion of its dimensions is one of the important forms of land use change that has many environmental, economic and social consequences. Changes in the natural pattern of drainage systems in urban and suburban areas, unprincipled use, development Impermeable surfaces are a major cause of urban flooding. The purpose of this study is to estimate the changes in discharge and runoff volume leading to the city as a result of increasing impermeable levels and urban development. In this regard, land use changes related to Abshouran watershed in three time periods have been tracked using Landsat images. EPA SWMM model was used to investigate changes in runoff peak flow. After calibrating the SWMM model based on the observed rainfall-runoff events, the calibration and validation results confirmed the conformity of the model simulations. Fitting of land use changes and corresponding floods during the study period shows that the area of urban areas in 2018 and 2001 compared to 1991 has increased by 189.68 and 60.79 percent, respectively. With increasing the percentage of urban impermeable surfaces, peak discharge and runoff volume for rainfall with a 20 year return period in 2018 compared to 1991, 198.82 and 188.04, respectively, and compared to 2001, 72.34 and 94.85 percent, respectively. Increased.