فصلنامه پژوهش های فرسایش محیطی
سال سیزدهم شماره 2 (پیاپی 50، تابستان 1402)

Arid conditions limit human activities, so they destroy the sustainability between ecosystem components in more degraded areas.The concept of sustainability is related to an ecological principle according to which sustainability is maintained if the exploitations are commensurate with the capacity. Furthermore, the persistence of sustainability in arid regions requires the assessment of features such as human factors, natural factors, flora, fauna, and interactions between humans and the environment. Therefore, the evaluation and monitoring of climatic indicators such as wind, plays an effective role in maintaining sustainability as an influential factor in arid areas. Since the second half of the nineteenth century, wind erosion has been recognized as one of the most widespread environmental problems, especially in areas with different seasonal climates and high levels of human activities. Therefore, this research aims to study the state of annual winds through the analysis of Golbal and sand Golbal, and to investigate the role of wind regime in determining the amount of discharge and the final direction of wind sediment movement, as well as the activity of these sand dunes using the Lancaster model in Khuzestan province. Using the results, it is possible to fight against the harmful effects of sand dune sediments in the study area.

 In this study, in order to investigate the wind situation and study the erosive winds in Khuzestan sandy regions, anemometric data were used in five synoptic stations (in the statistical period of 20 years from 2000 to 2019) around these areas. In addition, Sand Rose Graph software was used to calculate the values of sand transport potential (DP) by Freiberger speed classes method. In fact, due to the complexity and high volume of statistical calculations related to drawing diamonds, Sand rose software was used to calculate and draw the maps. To calculate the values of sand transport potential (DP) in different geographical directions, the Freiberger-Dean relationship based on the basic equations of Begnold and Leto-Leto was used. From the sum of DP values in different directions, the total sand carrying capacity (DPt) is obtained and in fact it is an indicator that represents the total wind energy to carry sand to the desired station. RDP stands for the amount or size of the output vector (resulting vector) of sand carrying capacity, which is obtained by summing the DP values in 8 or 16 different directions and shows the final status of sand transport in the study area.

Analysis of anemometer data by Golbad showed that in all stations except Omidieh, wind from the west has the highest frequency and the most abundant/frequent winds for Omidieh station are observed from the northwest. Examination of the sand carrying capacity in different directions (DP) shows that the highest wind carrying capacity in sand was related to Omidieh station with 2226.13 units, and the lowest was related to Ramhormoz station with 11 vector units. The average monthly RDP at selected stations is estimated from 10.8 in November to 130 in June, and 80% of annual RDP occurs between May and September. During the summer, stability in the RDP between stations occurs only in June and July. Comparison of the direction of vector output of sand carrying capacity (RDD) in the studied stations shows that the direction of sand movement in Bostan and Ramhormoz stations is to the east, Omidieh to the southeast and Ahvaz to the northeast and Shush to the north. There is a high homogeneity between the total sand carrying potential (DPT) and the homogeneity index for sand transport (UDI) at Bostan station.  The total sand transported in different directions (TSF) is shown in Ramhormoz station with a rate of 8/15+10 kg/m per year; among other stations after Omidieh station with 8/17+10, Ahvaz station stands with 3/95+10 kg per meter. Among these, the lowest sediment discharge belongs to Shush station. The total displaced sand (DSF) is located in Bostan station with 2892.92 kg and then Ahvaz station with 1948.51 kg. Also, in order to determine the amount of rainfall (P) and annual evaporation (PE), the annual rainfall potential and transpiration potential maps of Iran were used, which were prepared by the Forests and Rangelands Organization of the country. The activity map of the sand dunes of Khuzestan is also presented.

The results of the present research in the field of sand carrying potential and the frequency of erosive winds showed that the majority of the winds blow from the west in all stations, but in the Omidiyeh station, the winds flow from the northwest.The analysis of the movement direction of the sand dunes shows the complete compliance of the movement of the sand dunes with the winds of the studied stations. The study of sand carrying power values ​​in different directions (DP) shows that the highest wind power in carrying sand was determined at Omidiye station with 226.13 units and the lowest one was determined at Ramhormoz station with 11 units. Examination of the homogeneity index shows that Omidiyeh, Shush and Ramhormoz stations have multidirectional winds and Bostan and Ahvaz stations have bidirectional winds between 0.3 and 0.8 with moderate variability between the total sand transport potential (DPT) and the homogeneity index of transport direction. There is high homogeneity of sand (UDI) in Bustan station.The total sand transported in different directions (TSF) shows that Omidiyeh station with 8/17+10 has the highest potential, and the lowest sediment discharge belongs to Shush station. And the total displaced sand (DSF) in Bostan station is 2892.92 kg, followed by Ahvaz station with 1948.51 kg. In this research, an attempt was made to determine the potential activity level of sand dunes and distinguish active from inactive areas by considering the weather conditions and sand wind regime of Khuzestan province.

The U. S. Environmental Protection Agency lists sediment as the most common pollutant in rivers, streams, lakes, and reservoirs defined bank erosion as a natural geomorphic process or disturbance that occurs during or soon after floods. By producing sediment load, it causes pollution and reduces the quality of drinking water. In addition to that channel enlargement, bank instability, degradation of physical habitat and numerous other geomorphic responses accelerate the process of bank erosion Bank degradation is the result of a process that combines the erosive power of water and the effect of gravity. Bank erosion is one component of the natural disturbance regime of river systems and is integral to long-term geomorphic evolution of fluvial systems and ecological sustainability. Bank erosion is, therefore, a desirable attribute of rivers. In this study, Near bank stress (NBS) Rosgen shore for a part of Mereg Mahidasht river has been investigated. Mereg River Due to its location at high altitudes, high length and variable width, its meandering pattern and flood nature have been prominent features of this river. To suffer from general and localized erosion during floods and to reduce possible damages in the future by examining the erodible points

In order to conduct erosion studies, first a digital model of the altitude of the region, a 1: 1000 map of the region that covers the river and part of the flood plain has been used. The first stage was preparation of input data in ArcGIS using the HECGeoRAS extension. HEC-GeoRAS helps in creation of the data needed for the HEC-RAS model and the transfer of data between ArcGIS and HEC-RAS. The next stage was done within HEC-RAS (5.0.3) using the river geometry prepared in the previous stage. The final stage consists of analyzing the results from the HEC-RAS model within ArcMap. Three input parameters must be specified: stream geometry, flow data, and the model plan to create the flood and inundation maps of the Mereg river in HEC-RAS. In order to create the river geometry for HEC-RAS, elevation data were needed. High resolution digital elevation model was obtained from 1:1000 topographic map that was prepared by Navandish Water Processors Consulting Engineers Company Company. The HEC-GeoRAS extension was used to set up the necessary features that would be needed for the HEC-RAS model (i.e., stream centerline, bank lines, cross sections, etc.). In addition to elevations, Manning’s roughness coefficient values were applied to each cross sections using Cowan method. 44 sections of different river sections were selected and measurements were performed in HEC-RAS environment. And then , for bank stability from the Near bank stress (NBS) Rosgen NBS method is used. Two Rosgen methods have been used: (ii) Ratio of radius of curvature to bank-full width & (iii) Ratio of near-bank maximum depth to bank-full mean depth

According to the morphology and meandering pattern of the Mereg River, the total reach is 28 km and It is divided into four reach.
First reach: To study method of Near bank stress. In the first reach, 12 cross sections have been selected. In the first part, the river has a radius of curvature of 1.39. Bank Erosion of the Mereg River was low to severe in the first period.
Second reach: the average slope of the Merege River channel was 0.34%. The value of the radius of curvature is 1.64, which is considered as a meandering river. In this reach, 12 cross section have been selected to measure the degree of erodibility. Most arches were highly erodible.
Third reach: The slope of the canal in this part is 0.2%. The Mereg River has a radius of curvature of 1.7 and has a meandering pattern. And the rate of erosion of the shore in this section is estimated to medium or less.
Fourth reach: The number of cross section studied in the fourth reach is 5 and this reach of the river has a radius of curvature of 1.2 and has a straight pattern. The rate of bank erosion has been low and medium. 

Comparison of the two methods used to analyze the erosion of the Mereg River shows that the risk of erosion in all arches in the range of the river has been high erosion. And the reason is the intensification of hydraulic stress in these reaches. In the second and third reaches, the average bank erosion is calculated as medium to high. The reason for this is the reduction of the slope, which according to the longitudinal profile of the Mereg River, the average slope was 0.23%. In addition, there is a decrease in vegetation, an increase in the pattern of river winding and change of use and conversion of lands around the river to agricultural use in these periods.
Comparison of the results obtained from different methods of estimating the risk of erosion with field observations showed that Ratio of near-bank maximum depth to bank-full mean depth in the river Mereg Mahidasht was closer to reality and It is suitable for checking the degree of erodibility of the bank.

Improvements in classification accuracy over irrigated areas are essential to enhance agricultural water management and inform policy and decision-making on water management and land use planning. Advances in remote sensing technologies in conjunction with the emergence of big data and cloud-based processing platforms such as Google Earth Engine (GEE) are facilitating the classification of irrigated areas within improved accuracies in a timely and cost-effective manner, thus, enhancing the monitoring of these factors at both local and global scales. This process is aided by freely available high-resolution remotely sensed products and novel non-parametric machine learning algorithms for land use classification. This issue is very important in less developed areas such as the south of Kerman province where the economy is based on agriculture. Groundwater use in agriculture is soaring in arid and semi-arid regions such as Rudbar plain having a negative balance of underground water. In the same regard, this study applied Google Earth Engine to classify irrigated areas in Rudbar Plain.

This study used a non-parametric machine learning algorithm, i.e., Support Vector Machine Algorithm, to classify near-accurate irrigated areas using high-resolution satellite images. All the steps of this study, including preprocessing, classification, and accuracy assessment, were performed within the GEE platform. Preprocessing included cloud/snow masking and maximum imagery generation, and classification was based on the Support Vector Machine Algorithm. To this end, a GEE JavaScript code was used to access and analyze the data.  Time series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping. Therefore, in this study, we proposed a method for compositing the multi-temporal NDVI, in order to map irrigated areas with the Landsat 8 images in Google Earth Engine. The algorithm composites the multi-temporal NDVI into three key values including NDVI1, NDVI2, and NDVI3. So at first, the year was divided into three periods of 4 months. Then the maximum NDVI values for each pixel during each period were calculated. For this purpose, the maximum value composite was used to convert 16 days resolution NDVI data into maximum NDVI data for each period. Therefore, the three data sets, namely NDVI1, NDVI2 and NDVI3, which respectively correspond to the "first four months of the year", "second four months of the year" and "third four months of the year" were calculated. To this end, a GEE JavaScript code was applied to images. The classification process was automated on a big data management platform, i.e., the Google Earth Engine (GEE). Irrigated area is specified using false color combination with the selection of NDVI1, NDVI2 and NDVI3 indexes intended for the development of RGB. The existing datasets were used to train and validate the land cover. A random sampling method was used to balance the number of training point's classifications. Landcover categories were grouped into five types to separate cultivated areas from the rest of the land uses.

In this study, a new method for identifying irrigated lands was introduced using Google Earth Engine. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and google earth and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 81%. As a result of this study, maps of cropping patterns include five category 1: Date Palm trees and citrus fruits, 2: Potatoes and onions; 3: Tomato; 4: cucumber and 5: Sweet corn, Sesame and Sour tea. The areas of cultivation of category 1, category 2, category 3, category 4, and category 5 are respectively 170 km2, 283 km2, 133 km2, 56 km2 and 277 km2.  Also, the vegetation changes were investigated during the years 2013 to 2020. The results demonstrated the cover of low vegetation in 2013 and 2018 and the cover of high vegetation in 2020.

The combination of methods and approaches in GEE facilitated the rapid classification of more accurate irrigated areas with petabyte volume big data. The developed dataset of the cultivated areas has an overall accuracy of over 81%. Given that there is no specific pattern and plan for cultivating agricultural products in Rudbar Plain, the enhanced outputs of the irrigated area mapping are essential for policy and decision-makers to assess vast and complex irrigation systems’ performance in detail. They are critical for the accurate monitoring of irrigation activities from the field to transboundary or national scales. By examining the area under cultivation, it was found that seasonal cultivation is more popular with farmers than multi-year cultivation, and more attention should be paid to the marketing of agricultural products.

The increase in greenhouse gases in the last few decades has caused a disturbance in the global climate balance and climate changes (Aalst, 2006). Climate change and the increase in extreme conditions have caused disasters (Helmer & Hilhorst, 2006), increasing floods, and tropical storms (Haqtalab et al., 2012). Other consequences of climate change are changes in erosion and soil loss (Akbarian & Khoorani, 2022). Soil loss and sediment transport have always been one of the most critical problems of land management. Climate change and, consequently, changes in precipitation affect soil erosion and loss from various aspects such as the amount, intensity-duration, and distribution of rainfall (Gabris et al., 2003). As a result of climate change, the erosive power of rain is expected to increase (Sun et al., 2002). In order to predict the effects of climate change on the erosivity of rainfall, it is necessary to predict rainfall with climate models and then estimate the erosive power of rainfall with suitable erosion estimation models. Rainfall is the most crucial active driver of soil loss that displaces soil particles (Talchabhadel, 2020). It is difficult to accurately evaluate raindrops' characteristics with soil displacement and detachment. Therefore, the empirical methods are used based on rainfall. Some of these empirical models can be used with future climate data. The Revised Universal Soil Loss Equation (RUSLE) is one of the models used to predict soil erosion. Since precipitation is one of the main factors in this model, many researchers have used it to reflect upon the role of climate change on erosion changes. According to Goldies et al. (2022), using the RUSLE-GIS approach can estimate the current and future annual soil erosion rates in watersheds by reflecting climate change to the R factor based on the latest CMIP6 phase 6 climate forecasts.
Soil loss and sediment transport have always been one of the most critical problems of land management. According to the mentioned materials, the use of different models to estimate erosion is essential in the basins that are faced with a lack of data and statistics. The area studied in this research is the Minab watershed, which is vital from various economic and social aspects, especially the drinking water supply of Bandar Abbas in Hormozgan province. Accordingly, this research tried to determine the changes of erosion in these periods by examining the climate condition in the past, present, and future periods so that this information becomes available to decision-makers and managers for better planning. The Minab watershed is located in the range of 48°56° to 57°59° east and 27° to 28°32° north latitude. This basin is one of the sub-basins of Bandar Abbas-Sedich, located in the northeast of Hormozgan province and the south of Kerman province. The area of the basin is 10613 square kilometers. The Minab River, which is the result of the connection of the Jaghin and Rodan rivers, is considered the most important river in this basin. With the construction of the Minab Dam, its water resources are used to provide drinking water to Bandar Abbas.

This research used monthly data from meteorological stations, precipitation data extracted from different climate models and scenarios, soil data, and satellite images. The climate data are in different periods (base period 2020-2002 and future period 2020-2050). The RUSLE model was used to study erosion changes due to climate change. First, by preparing the RUSLE model invoices, the model was implemented for the 2010 and 2020 time periods. Next, two climate models, BCC-CSM2-MR and CanESM5, based on the sixth report and under scenarios 2.6, 4.5, and 8.5, were used to predict precipitation. After evaluating the models, the precipitation erosion layer in 2040 was prepared by the two models.

The results showed that the average rain erosivity layer has increased from 41.57 in 2010 to 52.01 in 2020. There is not much difference between the prediction results of the BCC-CSM2-MR model and the observed value, so it can be said that the model performed well in different scenarios. Among the used scenarios, the 8.5 scenario has the slightest error, and the 4.5 scenario is placed at the end. In the case of the CanESM5 model, there is no significant difference between the observed and the predicted values. Although the difference increases for more distant years, such as 2040, the results of different scenarios are similar. The output of the two BCC-CSM2-MR and CanESM5 models also indicate an increase in the rate of precipitation erosion in 2040. The average erosion in 2010 was 13.8 t/ha/y, which increased to 17 t/ha/y in 2020 and was predicted to increase to 17.3 t/ha/y in 2040.

The average rain erosion layer in 2010 was 41.57; in 2019, this value reached 52.01. According to the results, the analysis of precipitation data obtained from climate scenarios and models shows an increase in the erosivity factor of rain in the near future (2040). Considering the importance of the rainfall erosivity layer in water erosion, if other factors affecting erosion remain constant, in the near future, we will see an increase in erosion and soil loss in the Minab basin. The results of the RUSLE model in the three periods also indicate an increase in erosion over time. The spatial changes of basin erosion are a function of LS (topographic factor), and the rainfall erosivity factor (P) causing temporal changes in erosion.

Erosion is the main cause of wasting water and soil resources and causing natural damage. According to geological characteristics of erosion and sedimentation, it is very important to study the erodibility of the geological formations of the watershed to determine their constructive effects on sediment and runoff. Many factors affect soil erosion and one of the important factors is the erodibility of geological formations. Sediment that moves with water is called suspended sediment load, and the amount of suspended sediment material that passes through a river section in a certain period of time is called suspended load. The suspended sediment load (SSL) of a watershed, which passes through a certain section of the river, depends mainly on the climatic characteristics, the characteristics of the watershed and the capacity of carrying sedimentary materials. The input suspended load is one of the important and influencing factors on the amount of sediment input to reservoirs of dams and lakes. Determining the amount of sediment carried by rivers is important in many aspects. The calculation of suspended load is very important because of various reasons, one of the most important of which is the role of suspended sediment load in the quantitative and qualitative management of surface water resources. Therefore, the distribution and transportation of suspended sediment load (SSL) in rivers have a significant effect on the water resource management, design of hydraulic structures, river morphology, water quality, and aquatic ecosystems.

The present study was carried out to evaluate sedimentation and runoff production of Asmari and Gachsaran Formations in Ghaleh Gol watershed around Khorramabad city, from Lorestan province, Iran, located between 48° 21 '2" and 48° 33 '1", and between 33° 15 '43" and 33° 21 '15" N with an area of 10.76 km2. The studied area has a semi-arid climate with a mean annual rainfall of less than 500 mm.  This study aimed to measure suspended sediment load (SSL) and surface runoff during the November 30th, December 2th and 16th 2020 and also on the 12th of March 2021; for this purpose two square meter plots were used. A tank was installed at the plot outlet to collect sediment and surface runoff. After the rainfall finished, the volume of water and sediment collected in the tank installed at the end of the plot was measured.

The results showed that during the mentioned rainfalls, the average volume of water output from Asmari and Gachsaran Formations were 1.802 and 1.345 liters, respectively; the average output sediment for these two formations were 1.133 and 1.048 g /l, respectively, ant the total output of suspended sediment load (SSL) was 3.083 and 2.227 gr, respectively from Asmari and Gachsaran Formations. Finally, the obtained results suggest that the Asmari formation has the highest erodibility. Also, according to the results, the Asmari formation has the highest flooding level in the study area.

According to the results, considering erodibility and flooding features, Asmari Formation has higher sensitivity compared to Gachsaran Formation in the study area. In fact, the obtained results showed that Asmari formation based on the prioritization of erodibility and flooding has the first rank, which must be prioritized for conducting management operations in the Ghaleh Gol watershed.

Biocement is a method that stimulates native soil bacteria to connect soil grains through a procedure known as microbially-induced calcite precipitation (MICP). It can use microorganisms to produce a tough and renewable building material with the slightest impact on the environment. MICP has been exhaustively explored using different types of bacterial species to enhance the compressive strength of cement composites such as mortar. Sporosarcina pasteurii is the most studied species of Bacillus bacteria for MICP utilizations on cement composites. S. pasteurii are urease-creating bacteria that have the prowess to induce sufficient calcium carbonate precipitation for bio-cementation to materialize in concrete structure. The mechanism of bio-cementation of the bacteria is regulated by urea hydrolysis.

Bacillus megaterium and Sporosarcina pasteurii bacteria were chosen in in this research study with a focus on the Sistan silt soil area (South East of Iran). Bacterial solution consists of Nutrient broth and bacteria. Nutrient broth consists of Peptone, Yeast extract and sodium chloride. In this method, the temperature to be maintained in the oven is 37 for 24 hours for the solution and then the experiment was conducted at a temperature of 17°-20°c atmospheric pressure. This study aimed at achieving a more realistic environmental condition for the application of MICPA control specimen containing commercially bought Bacillus megaterium which was used to compare the MICP efficiency of the indigenous bacteria. A blank specimen (without any bacteria) was subjected to the same temperature, pH and the cementation solutions.
Initially, bacteria was added to the fine soil and was mixed properly, which was followed by addition of the cementation reagent. The amount of bacteria and cementation reagent to be added. Direct shear and ultrasonic tests were performed on the silty soil. Proper mixing was ensured for proper fixation and distribution of bacteria in the soil. Fine Soil was compacted and was tested. Since the treatment duration was a parameter, soil samples of given bacterial and molar concentrations were allowed for curing or treatment duration of 8 weeks. The treatment duration was managed in order to provide sufficient time period for the chemicals to react and further allow CaCO3 precipitates to develop.

Two microorganisms (S. pasteurii and B. megatarium) were proposed for soil improvement by MICP technique. In this particular treatment, fine soil as clayey sandy silt or loam in Sistan plain, S. pasteurii was evidenced to be the most effective microorganism for MICP treatment.
The MICP-treated soil specimen exhibited moderate improvement in shear strength. This improvement is estimated for a specified density of soil specimen. Density on strength improvement by MICP indicated little increase.
The maximum amount was observed for the soil specimens treated with the microorganism S. pasteurii and B. megatarium after the biocementation; the velocity of ultrasonic waves increased and the internal angle of friction in bacterial sandy samples increased as well. Tests were conducted to evaluate the feasibility of using ultrasonic testing for stabilization applications. The results indicated the fact that some calcite forming microorganisms were present in the original soil.

A series of direct shear and ultrasonic tests were conducted to examine the behavior of the biocemented silt specimens of Sistan plain. The tests were directed towards the stiffness of biocemented silty specimens with different agents. The specimens cemented by two different microbial products and non-bacterial soil were prepared and tested under 25, 50 and 100 kPa vertical stresses. The variation of the deviatoric stress with the local vertical stress was registered.
 The shear stress behavior of the specimens significantly changed with different times of microbial products for cementation from the starting point of samples preparation. A significant increase in the shear stress values velocity of Ultrasonic Waves was observed as the duration of biological treatment increased.
The researchers consider that the biological treatment of the specimens under confining pressure plays an important role in increasing the strength and internal angle of friction. The fact is that an increase in the duration of the biological treatment process, which corresponds to the number of urea-calcium chloride cycles, resulted in an increase in the amount of calcite precipitating on the soils grains. These results were verified via increasing velocity of Ultrasonic wave. It seems that the amount of Ca increases in the treated samples as the treatment period increases.
A slight decrease followed by a sharp decrease in the stiffness of biocemented specimen was observed, while a gradual decrease in the stiffness of grain soil was observed. The sharp decrease, due to the bonding breakage, is a distinctive behavior of a typical artificially-cemented silt.
In general, velocities increased with curing time, and the increase from the day of compaction to 7 days was more significant than the increase from 7 days to 21 days. As the curing progresses, further reactions occur between the soils and the stabilizing agents. These reactions generally result in increases in the stiffness of the soils. The velocity of wave propagation increases with the increased stiffness of the soils. Therefore, MIBC can be an effective method for the stabilization of silt soil in Sistan plain.

Mass movements are a type of morphodynamical phenomena that are usu ally related to various factors and occur on slopes in mountainous areas. Every year, damages caused by landslides lead to financial loss and death over the world (Fathi et al., 2018). According to previous studies, landslides cause 17% of the world's natural disasters. Mortality rates from 1903 to 2004 for different continents, including Asia, United States, Europe, Africa, and Australia were 29%, 39%, 30%, 1%, and 1%, respectively (Kohorest et al., 2005). Over the last three decades, several research studies have been conducted on landslide susceptibility mapping using different methods for developing their classification. All mapping methods are classified into five different groups including landslide distribution analysis, qualitative, statistical, deterministic, and frequency analyses (Vanvestern, 2003). Iran is prone to landslide phenomena due to natural conditions such as mountain topography, high tectonic and seismicity activity, geological and climatic diversity. Environmental factors affecting the occurrence of landslides are slope degree, aspect, plan curvature, elevation, land use, lithology, distance from the road, river and fault, topographic moisture index, slope length index or sediment transport and vegetation cover (Silakhori et al., 1400).

Firstly, 134 points were identified using the Iranian landslide database and field survey in order to prepare a landslide susceptibility map using Bayesian theory. In the present study, 12 factors were used including elevation, slope and aspect, plan curvature, distance from the fault, road, and river, land use, geology, sediment transport index (STI), topographic wetness index (TWI), and vegetation cover. Then, topographic (1:50000), geology (1:100000), soil maps, and satellite imagery (Indian Remote Sensing) for 2012, were prepared and classified in ArcMap and ENVI environments. In order to evaluate Bayesian theory in landslide risk analysis, the relative performance curve of the relative efficiency of variables (ROC) was applied. This index is used to determine the accuracy and efficiency of the model (Egan, 1975; Williams et al., 1999). The area under the ROC curve represents the predicted value by describing its ability which accurately estimates the occurred events (landslide occurrence) and its non-occurred events (non-landslide occurrence). Therefore, the area under the curve is used as the model accuracy assessment. In the present study, 134 points of the landslide’s phenomena were used for modeling (70%) and accuracy assessment (30%) (Pourghasemi et al., 2013).

The results of the factors affecting the occurrence of landslides using Bayesian theory in the study area showed that most of the landslides occurred in the class of 15-30 degrees with a weight of 1.28, which is also reported by Eracanoglu and Gokceolu (2004). The reason is that human intervention on these slopes causes more susceptibility (Yalcin et al., 2011). The study of aspect shows that most of the landslides occurred in the west and south directions with a weight of 2.21 and 2.41, respectively, which is confirmed by the results of Shams and Alizadeh (2019). The results of plan curvature showed that most of the landslides happened with a weight of 1.1 in convex slopes, which is close to the results of Pourghasemi (2013). Convex slopes usually have the highest landslides which is also reported in previous studies (Vanvewsten et al., 2003; Jaaferi et al., 2014). The altitude of 500-1000m covered by sandstones, chile and siltstones showed a significant relationship with a high number of landslides (Ayalew & Yamagishi, 2005). Among different land uses, rangeland (weight of 3.48) indicated the most significant relationship in landslide occurrence, which is reported by Shams and Alizadeh (2019).  
The results of distance from roads, rivers, and faults showed that most landslides occurred at distances of more than 0-100 meters which confirms the results of previous studies (Pourghasemi & Mohammadi 2016). Also, the relationship between vegetation cover and landslides showed that the highest percentage occurred in class 0.5-0.3 with a weight of 3.56. In addition, the highest slippage for topographic wetness index and sediment transport index (river capacity) occurring were related to 6.39- 11.29 and 11.84-19.6 classes (weights of 1.009 and 1.17), respectively. The area under the curve was calculated at 85.56% in the model validation for the Bayesian model, which was classified as a very good performance. Therefore, the results of our study can play an important role in the management and planning of the Talar watershed.

Geomorphological features play an essential role in changing the reactions of a basin and they can have significant effects on the erosion and sedimentation potential of watersheds. Meanwhile, the influence of geomorphological facies is significant in changing the amount of soil erosion, and its effects are manifested in the amount of sediment production. Establishing a relationship between the morphology of waterways and the active processes in them helps to correctly understand and predict their response to natural and human changes. Therefore, it is necessary to identify geomorphological phenomena that can influence the structure of waterways. Today, with the use of computers and the existence of capable remote sensing software and geographic information systems, the necessary calculations can be performed with high speed and accuracy. The purpose of this research is to compare the waterway network extracted from digital models of different heights.

Using the algorithms available in the software, a flow direction grid was created and a cumulative flow grid layer was extracted from it. For each cell, the current accumulation layer determines and ranks the number of cells that direct their current to the mentioned cell according to which the cells with the highest numerical values ​​correspond to the concave lines and the cells with values ​​close to zero and zero correspond to the ridge lines. To extract the waterway network from the studied area, DEMs 10, 30, and obtained DEM were used by ARCHYDRO software, which finally produced three maps. The waterway network was extracted from the three mentioned DEMs. In this study, the drainage network map, extracted from the digital topographic maps provided by the country's mapping organization, was used as a measure close to the ground reality. Based on the constructed waterways, the border of the main closed rivers and the area and perimeter of the main basin were obtained.

According to the results of the total length of the waterways, it can be seen that the cell size of 10 meters has the largest value so that the model calculates the length of the waterways more accurately. According to the drainage density values, it is observed that the amount of drainage density decreases with the increase in cell size. This problem indicates that with the increase in the cell size, the lower ranking waterways are removed, and considering that the drainage density is obtained from formula (1) and in this formula, the number of waterways and their lengths have great effects on the drainage density, so it can be seen that as the cell size of the height digital model increases, the drainage density decreases. The drainage density obtained using a cell size of 10 meters is the closest value to reality. Using DEM10 and Arc hydro software, the waterway network map was extracted and compared with the reference map (waterway network map of the Mapping Organization) of the country.

According to the obtained results, by increasing the cell size of the digital elevation model, small details are not considered and the accuracy of the model decreases; as the pixel size increases, the sub-channels are removed, which affects the output parameters. This model reduces the length of waterways and the density of drainage, which is consistent with the results of Davari and Hack's research. As the cell size increases, the length of the main waterway decreases which confirms the results obtained by Ashurlo et al. (2008). However, the drainage density obtained using a cell size of 10 meters is the closest value to reality. Govan et al. (2001) and Hosseinzadeh and Nadaf Sangani (2013) also achieved similar results in their research. The results showed that with the increase in the cell size, the calculation of the length of the waterways is associated with error to the extent that with the increase of the cell size by more than 10 meters, the error rate becomes more significant and far from the actual amount. The parameters obtained from digital height models with a cell size of 10 meters in the Arc hydro model had the closest results to the real standard. Based on this, Zhang and Montgomery (1994) and Yang et al. (2010) considered 10 meters as an appropriate index in their research. According to the mentioned cases, the correctness of this theorem can also be visually verified in the waterway network maps obtained from the digital models of 10 and 30 meters height, as well as the waterway network obtained from the topographic map 1: 50,000 regions compared to control waterway network map (the waterway network of the country's mapping organization). It was also observed that the smaller the cell size, the more accurate waterway network of the region can be obtained. The results of satellite image processing show that among the three indices of NDVI, NIR and LWC, the NIR index shows a more accurate waterway network pattern. This index showed that 90% of waterways created by this index are consistent with waterway network. Also, in the surveys that were conducted in the plains and mountains separately, it was shown that 94% of the created waterways in the plains and 91% in the mountainous areas are the same as the control waterways, which confirms the high ability of this index to detect waterways in plain areas and to prevent the occurrence of errors in the identification of waterways.

In this study, the sedimentation process in QareChai River (located in Markazi province) and the possibility of sand mining and construction activities in the margin and floodplain areas was evaluated. To do this, by simulating the river flow, the rate of erosion and sedimentation was simulated in a length of 29.8 km near the Khondab city. After preparing the required and basic information and data related to bed materials and sediment gradations, the river sediment flux and distributions along the river were investigated using the HEC-RAS model. After calibrating the model, Wilcock sediment transport formula was selected as the most appropriate equation for estimating sediment discharge with an error of 6% in comparison with the values ​​measured at the hydrometric station. Accordingly, the amount of bed changes in the level of each river section, the amount of sediment and the changes in the longitudinal profile of the river over a 10-year period were simulated. The results showed that during 10 years, there was an average of 13 cm of erosion. The average erosion in the intervals of 6 km from upstream to downstream is 18, 21, 14.8 and 3.2 cm, respectively. Therefore, it is impossible to extract river bed materials in this period. In addition, the creation of a hole with a length of 6 km, a wide of 50 m, and depths of 20 and 50 cm from the deepest point of the river at a distance of 6 km downstream showed that these holes will remain constant after 10 years and will not return to their original conditions before mining. Therefore, when the erosion rate reached its minimum value, it will be possible to extract sediment at a distance of 6 km downstream.

Soil erosion is one of the most significant environmental problems in the world; it is a threat to food security, the environment, natural resources and causes socio-economic problems. In this regard, the importance of estimating the amount of erosion to adopt appropriate management methods in different areas has been established. Among the existing models of soil erosion and sedimentation, models that can predict soil erosion and sediment performance can be useful and widely used. In this regard, the IntErO model, which is a graphical computer method based on the EPM erosion potential method embedded in its algorithm, can be applied.

This study aimed to estimate the severity of soil erosion and runoff using the comprehensive, rapid, and effective IntEro model in the Kahouristan watershed. After collecting basic information, 26 input variables were extracted and calculated using topographic maps, geology, geology, land use, and climate data. In this regard, to estimate the inputs of the IntErO model, at first, the physiographic and topographic features of the Kahouristan watershed were extracted using a 1: 25000 digital map and the boundary of the information range. Also, the lowest and highest elevation points, the lowest value of the alignment line, and the equal distance between the alignment lines were determined. Geological and land use characteristics of the region Geological and land use maps were used. The maximum probable precipitation (PMP) method was used to achieve the height of torrential rainfall. Finally, after preparing all the inputs of the IntErO model, the computational data for the Kahouristan watershed were entered into the graphical environment of the software, and the components related to erosion and sediment as well as the maximum outflow from the Kahouristan watershed were estimated using the model.

The results showed that the drainage density was 1.35 in the Kahouristan watershed using the IntEro model, and the infiltration coefficient of the watershed and the vegetation cover coefficient were 0.67 and 0.84, respectively. Also, the coefficient of water retention in the studied area was found to be 0.91 and the potential of water flow during flash floods was found to be 6597.04. On the other hand, the erosion energy coefficient in the study area was found to be 102.12 using the Intro model, and the total erosion in the mentioned area was equal to 3456636.72, and the erosion coefficient of the Kahouristan watershed was 0.918. The amount of real soil erosion and special real soil erosion in the studied watershed is 631152.53 m3/year and 150.14 m3/Km2.year, respectively. The re-sedimentation coefficient of sediments due to erosion was 0.183.   

The results of the current research showed that the permeability coefficient of the study watershed was 0.67, which according to the value of the vegetation coefficient which was 0.84, it can be said that the appropriate cover in the area has caused the permeability. However, the drainage density in the study area was 1.35, which indicates the resistance of the soil in the area against erosion.
Also, examining the results shows that the maximum output flow estimated by the mentioned model for the study watershed is about 1778 m3/s. Examining the maximum outflow with the 21-year statistics of the Kahouristan hydrometric station shows that the maximum outflow from the study watershed is about 1635 m3/s, which indicates the appropriate performance of the model in estimating the maximum outflow from the study watershed. Based on this, it is recommended to use this model in estimating the output flow and other characteristics in the study watershed. Another hand, the results showed the predominance of rill erosion in the area and the transfer of sediments to the outlet of the basin. According to the model results, estimating the amount of erosion in the study area was not accurate enough. However, estimating the maximum output current from the basin based on the available statistics indicated good results; in other words, the use of this model is recommended in estimating the output current and other characteristics, but in areas having similarities with Kahouristan watershed conditions, heavy and short rainfalls for estimating the amount of erosion is not recommended due to its inability to provide appropriate results. Finally, it is suggested that other models of erosion and sediment assessment for the Kahouristan watershed and other watersheds with similar conditions in this respect be studied and compared with the IntEro model as well.

Due to erosion, soil structure is destroyed, plant elements are removed from the soil by runoff and destroyed, soil fertility is reduced and, as a result, the production and productivity of the soil is reduced. The use of modifiers such as polyacrylamide while reducing runoff production and soil erosion can be useful in reducing the wastage of soil nutrients, especially in sloping lands. The aim of the present study was to investigate and compare the rate of sediment and runoff production and nutrient loss in three slope positions and four concentrations of polyacrylamide using a rain simulator. Direct measurement of runoff and sediment, efficiency and repeatability in different intensities, durations and amounts of rain are among the advantages of using a rain simulator in investigating surface runoff, erosion, and sediment (Jahanbakhshi et al., 2016). Therefore, in order to investigate and study soil erosion and direct measurement of runoff and sediment, rain simulators can be used.

The present research endeavor was conducted to investigate the effect of using different amounts of anionic polyacrylamide superabsorbent in controlling the erosion and wastage of soil nutrients in sloping lands. The experiment was performed as a factorial experiment in a completely randomized design with three replications. The first factor of the slope was determined in three positions (slope toe, slope foot, and shoulder slope) and the second factor was the concentration of polyacrylamide at five levels (0,0/4, 1, 3, and 6 g/m2). The test area was located on the campus of Mohaghegh Ardabili University being exposed to a 10-minute rainfall with an intensity of 35 mm/hour; it was checked that the intended rainfall was simulated by a rain simulator. A total of 45 experiments were performed by the rain simulator on plots measuring 2 m2. After conducting each rain simulator test in the plots, runoff and sediments were collected in a collection tank and the amount of sediment, runoff volume and amounts of sodium, potassium, calcium, magnesium, phosphorus and organic carbon were measured in the laboratory.

The effects of slope position on all soil characteristics were significant (P <0.01) increasing from 0.35 kg/ha to 0.55 kg/ha. The effect of slope position on organic carbon loss was significant at the 5% level and other elements at the 1% level. The effect of polyacrylamide concentration was significant only on phosphorus loss at the level of 1%. Also, the interaction effect of slope and concentration on calcium loss was significant at the level of 5%. By changing the position of the slope from the toe slope to the foot slope, the average runoff volume increased from 3.84 to 14.8 liters, and the average phosphorus loss increased from 0.035 kg/ha to 0.55 kg/ha. The effect of PAM in higher concentrations was greater than in lower concentrations in reducing runoff, sediment, and then the loss of nutrients in the soil. The results showed that the maximum production of sediment due to the simulated rain on the surface of the plot is 192.36 grams per square meter. This is the value of the control plot at the foot slope (S0) which is significantly different from other treatments. The lowest amounts of sediments were related to the treatment of 6 grams per square meter of PAM in the position of the toe slope (F6) with an average production sediment of zero, and the treatment of 6 grams per square meter of PAM in the position of the toe slope (F3) with an average sediment production of 1.69 grams per square meter.

The highest amount of sediment and runoff was produced at the foot slope. The greatest loss of nutrients was observed at the foot slope. It was observed that the effect of PAM in higher concentrations is greater than in lower concentrations in reducing runoff, sediment, and then the loss of nutrients in the soil. It can be concluded that the use of anionic polyacrylamide has an important effect on reducing soil erosion and consequently reducing the wastage of elements such as phosphorus and organic matter in sloping lands, because the release of these elements from the soil mainly happens attached to the soil particles. This research showed that the effect of PAM in reducing runoff, sediment and then the loss of nutrients of soil, in higher concentrations is greater than in lower concentrations but from a certain concentration onwards (3 grams per square meter) the impact of PAM does not increase.

Nowadays, land use change increasingly causes environmental problems and usually, in the first step, affects the soil properties and causes a decrease in the quality and an increase in the destruction, and erosion of the soil. Land use is defined as the sum of management and planning activities and inputs that humans do in a specific type of land cover (Ellis, 2021). The improper use and management of land resources, which has led to soil degradation, can have a great impact on the development of sustainable agriculture and is recognized as a serious global challenge for food security and ecosystem sustainability (Ebabu et al., 2020). Changing the use of forest lands and pastures for agriculture purposes has become one of the significant concerns in the world causing environmental degradation and climate change. Studying the effect of land use on soil properties provides the possibility of identifying sustainable management and preventing future soil degradation. The knowledge about the effect of land use change on soil properties, as one of the most essential components in sustainable agriculture in Jiroft, which is considered one of the important agricultural regions of Iran, is very limited. Therefore, this study was carried out to investigate the effect of different land use on some physical and chemical properties of the soil and to prepare distribution maps of these properties in the Konarsandal region of Jiroft.

In this research, the effect of the type of land use was studied in a part of the lands of Jiroft Plain (around the historical hills of Konarandal). For this purpose, by sampling surface soil in four types of land use, including agricultural land, abandoned agricultural land, date garden, and natural forest, some of the physical and chemical properties of soil including the percentage of sand, silt, and clay, electrical conductivity, pH, absorption ratio of sodium, calcium carbonate, organic carbon, bulk density, liquid limit (LL), plastic limit (PL), specific surface area (SSA), cation exchange capacity (CEC) and hygroscopic water content (HWC) were studied, and the spatial distribution maps of some parameters were obtained. In terms of topography, the studied area has a relatively uniform slope and its physiographic unit is in the form of alluvial plains. The dominant forest land cover in this area mainly includes Prosopis and Tamarix aphylla and the dominant vegetation cover of the abandoned agricultural land includes the Salsola soda. Also wheat and melon are mainly cultivated in the agricultural lands. According to the announcement of the residents and owners in the studied area, the history of agricultural lands (date garden and arable land) is more than 20 years and the irrigation system of these lands is in the form of flooding. 26 samples of the surface soil (0 to 20 cm) were taken from each land use (104 sampling points). For statistical analysis of the data, SAS9.1 was used and averages were compared with Duncan’s test (p<0.05). Correlation between the data was done using SPSS16 and Variowin2.2 was used to calculate and draw the Variogram maps.

The results showed that the type of land use changed all the studied parameters. The highest percentage of clay (46%) was observed in the abandoned agricultural land. Changes in the percentage of sand, silt and clay in different land uses could be caused by the type of management conducted on the process of soil particle loss. With changes in soil textural characteristics (sand, silt, and clay), the SSA, LL, PL, HWC, and CEC had also changed. The kriging maps also showed the uniform distribution of these features well. The soil quality in the abandoned agricultural land was not very suitable compared to other land uses. The amounts of EC and organic carbon in this land use were about 2 times more and about 20% less than agricultural lands, respectively. The coefficient of variation of particle size distribution (sand, silt and clay) and the organic carbon among the studied soil variables in the region were relatively high, which could be attributed to the land use changes, differences in plowing, management operations and surface soil erosions.

Differences in the separation of soil particles in different land uses can cause differences in the distribution size of soil particles and even soil texture (Tallen and Yerima, 2018). The result that was also observed in this study. Other properties of the soil such as LL, PL, SSA, CEC and HWC were also different in the studied land uses which could be related by positive and significant correlation (r>0.7**) with clay content. The abandonment of agricultural lands had caused low quality and fertility of soil in this land use; this issue could be due to high evaporation in this area which can increase salinity, and also because of little vegetation in this area which decreased the amount of organic matter of soil. It is suggested that in order to preserve the soil properties of the abandoned agricultural lands, if the conditions in terms of water supplement are available, this land use should be restored as agricultural land, or according to the amount of seasonal precipitation in this region, these lands be cultivated by drought-resistant plants such as Tamarix aphylla.

   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.