## 1 Introduction  
Soils are very fragile and vulnerable to environmental impacts [1, 2]. Soil erosion is one of the most severe global environmental challenges, with a considerable impact on soil and water quality [3, 4]. Soil erosion is the removal and transportation of surface material caused by natural and man-made processes [5]. Soil erosion can be accelerated by human factors such as land use change, deforestation, overgrazing, and over-cropping [6, 7]. These actions affect soil properties and decrease the land cover that provides stability to the land. Soil erosion probability is highly impacted by climatic, topographic, LULC, plant, and other biophysical parameters, such as soil characteristics. These factors contribute to the uncertainty and complexity of the soil erosion phenomenon [8, 9]. Because the behavior of biophysical factors in one location may vary from that of another, sustainable erosion management necessitates unique region studies [10, 11]. Soil erosion is mostly caused by natural causes such as water and wind. Water affects soil erosion by precipitation intrusion in sediments and runoff on the earth's surface,resulting in rills and gullies [12; 13]. Soil erosion and its negative impacts on the environment and agricultural land have increased dramatically in recent decades in developing countries as a result of poor policy planning for farmland, population growth, and urbanization [14]. In addition to polluting air, water and destroying soil, erosion has a big effect on how the world's population changes [15; 16; 17]. Because of this, it's important to come up with different ways to reduce the effects of erosion. Therefore, the assessment of soil erosion is a crucial issue [18; 19]. Researchers employed many methodologies and models to develop the Soil Erosion Susceptibility Map (SESM) [20]. Previously, scientists employed qualitative techniques such as Weighted Overlay Analysis (WOA) to develop SESM for the region of interest in order to identify soil erosion high-risk areas. Researchers used the WOA approach to analyze several predisposing variables such as rainfall, topography, and soil erodibility index and assign a value to each factor based on its effect on soil erosion [21]. Aside from the WOA method, various models such as the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE), the Modified Universal Soil Loss Equation (MUSLE), the Erosion Productivity Impact Calculator (EPIC), the European Soil Erosion Model (EUROSEM), and the Water Erosion Prediction Project (WEPP) have been widely used by researchers to produce soil erosion maps. These approaches are employed by researchers for a variety of reasons, including their low cost, simplicity of implementation, and compatibility with GIS [22]. These approaches include drawbacks such as uncertainty and inaccuracy in the model's real and predicted values [23]. Later, as geospatial technology advanced, researchers used GIS-based quantitative approaches to simulate soil erosion [24]. GIS-based technologies have been approved for computing and mapping watershed zones susceptible to soil erosion, and can help in the management of soil erosion and related dangers [25]. The causal elements, namely the watershed features, should be thoroughly evaluated [26]. A previous study revealed that several erosion-causing elements and approaches have been applied in soil erosion susceptibility mapping. Some of the models used in previous studies include logistic regression [27; 28], the Analytical Hierarchy Process (AHP) technique [26], sensitivity analysis approaches [29], weighting overlay [30; 31; 32], a soft computing method [33], and artificial neural-network evaluation methods [34; 35; 36]. In the present study, we integrated GIS-based statistical models such as WOE and FR to forecast possible soil erosion areas.