## 1 Introduction  
In hilly areas throughout the globe, landslides inflict massive fatalities and economic damage. To stabilize or manage slopes, methodical and rigorous procedures are always used to prevent or reduce mass movements [1]. Because this is seldom recognized, new and more effective methods are needed to improve awareness of landslide risk and allow reasonable choices to be made about how to allocate money for landslide risk management [2]. Several studies on landslide forecasting and risk mitigation have been conducted. Landslide risk analysis and hazard assessment have been a significant topic in hydrogeological research [3, 4]. In recent years, the use of information and geospatial technologies, such as remote sensing and geographic information systems (GISs) has made a significant contribution to landslide hazard assessment research [5]. Furthermore, the value of quantitative landslide risk assessment has been acknowledged, as it serves as the foundation for maintenance, mitigation measures, and resource allocation [6]. Topography, geology, hydrogeological conditions, plant cover, and precipitation are all variables that influence the incidence of landslides in different geographic regions [7]. It becomes more difficult to evaluate landslide susceptibility in these situations because varied and significant quantities of spatial data from the regional area must be collected and taken into account throughout the analysis process [8, 9]. Understanding watershed water quality is critical for the long-term management of water resources; as water combines with soils and foundation rocks, the chemical properties of the water in a reservoir alter affecting stream water quality. There have been numerous cases of a single landslide and a neighboring stream polluting nearby waterways in recent years. Water pollution caused by landslides occurs when pollutant-laden landslide debris reaches a river or other body of water [10]. Organic and inorganic elements, such as sediment, can enter drinking water supplies following landslides. An unprecedented long-term water boiling advisory instruction was imposed by the Regional District of Greater Vancouver in 2006. One theory holds that landslides that occur near drinking water storage areas cause deteriorated water quality [11]. The statistical connections between landslide-inducing variables and sites of previous landslides are assessed using data-driven techniques and then quantitative forecasts for landslide-free regions with comparable circumstances are produced [12]. These techniques are referred to as data-driven approaches since they rely on historical landslide data to determine the proportional significance of each component. The three predominantly utilized data-driven approaches are multivariate statistical techniques, bivariate statistical tools and artificial neural network analysis [13; 14]. In bivariate statistical analysis, each confounding variable, such as land, slope and geology, is coupled with the locations of landslide occurrences, and the weighting values for each class of parameter are computed. Multivariate statistical methods are used to evaluate the link between an independent variable and a collection of dependent variables [14; 15; 16]. However, statistical models often neglect the temporal elements related to landslides and are not able to anticipate the effect of variation in landslide-controlling circumstances (e.g., alteration in land usage and water table) on landslides [17; 18]. Physically based landslide susceptibility assessment techniques describe the occurrence process using physical landslide models [19]. The techniques estimate slope instability using geometric and geotechnical data. Unlike data-driven techniques, site or laboratory tests and physical slope model findings may be used to assess slope stability independent of landslide incidence [20]. The consistency of the slope is determined by analyzing the forces acting on it using a physical slope concept, such as an infinite slope model [17; 21].