## 3 Materials and Methods
### Methods
#### 3.2.2 Production of the Thematic Data Layers  
Elevation, slope, aspect, curvature, stream, precipitation, LULC, soil, and road network were all considered in this research to assess the impact of the aforementioned causative factors on soil erosion. **(i)**: **Elevation:**: The elevation of the land influences the type of plants and the pattern of rainfall [43]. SRTM data with a spatial resolution of 30 m was used to obtain the elevation of the research area. Using the natural break technique, as shown in Figure 3a, the elevation was separated into different classes. **(ii)**: **Slope:**: Slope is an important element in soil erosion because the angle of the slope impacts water penetration and velocity; thus, gentle slopes and flat areas have less soil erosion than steep gradients and slope length [44]. As illustrated in Figure 3b, the slop of the research region was estimated using an SRTM DEM with a resolution of 30 m and reclassified into five classes using ArcGIS 10.8. **(iii)**: **Aspect:**: The variable has a major effect on plant growth and health, evapotranspiration, and moisture content, all of which influence soil erosion [12]. As illustrated in Figure 3c, aspects were obtained from SRTM DEM and categorized into nine classes using ArcGIS 10.8. **(iv)**: **Curvature:**: Details of curvature investigation provide useful information on the geomorphology of the investigated area [45]. As shown in Figure 3d, the curvature of the study region was calculated from the SRTM DEM and reclassified into three groups in ARCGIS 10.8. **(v)**: **Drainage:**: Stream channels have a significant impact on the shear strength of rocks and the erosion of sediment [28]. The stream network was extracted from the SRTM DEM and a five-class buffer was applied to the stream network to understand the relationship between soil erosion and drainage network, as illustrated in Figure 4a. **(vi)**: **Rainfall:**: Precipitation is a crucial causal feature of soil erosion because it causes particle dissociation and accelerates soil erosion [46]. The erosivity of precipitation is a primary driving element for sheet and fill erosion. The present research area's rainfall map was generated from CHIRPS data using a machine learning approach. As illustrated in Figure 4b, the final rainfall map was categorized into five groups using the ArcGIS 10.8 platform. **(vii)**: **UIC:**: LULC change has a significant impact on soil erosion, independent of other predisposing factors such as climate, soil properties, and topography [47]. The research area's LULC map was generated in GEE using a machine learning approach employing high resolution data. As illustrated in Figure 4c, the LULC map was exported into ArcGIS and reclassified into five groups to examine the various classes of LULC with soil erosion. **(viii)**: **Geology:**: The composition of different types of rocks, such as volcanic, sedimentary, and metamorphic rocks, as well as their geological formations, have varying effects on soil erosion [48]. Figure 4d depicts the lithological map of the research region, which was digitized from the Northern Geological Map of Pakistan. **(ix)**: **Fault:**: A fault is a lithological feature that has deformed the rocks in the studied region. Figure 5a shows a fault map that was digitized from a geological map of North Pakistan. Following digitization, five buffers were used to estimate the association fault with soil erosion. **(v)**: **Soil:**: The texture of the soil surface is an important predisposing factor of soil erosion since the texture and structure of the soil impact soil resistance to erosion because structural stability and plant cover reduce soil erosion [43]. Figure 5b depicts the soil map of the study region obtained from a soil survey in Pakistan. **(vi)**: **NDVI:**: Vegetation cover reduces soil erosion and may affect the sedimentation pattern [49]. In this work, we calculated NDVI in GEE from 2017 to 2022 using a machine learning method.