## 1 Introduction  
Disastrous natural hazards such as landslides have brought enormous casualties and economic losses in the past. In hilly areas, the essential requirement for ensuring people's safety is the identification of high-risk landslide-prone zones where development should not be conducted, and where soil stabilization works should be conducted. Nearly 12.60% of landslides have occurred in the Indian Himalayan region, and all areas can be detected as being landslide-prone. In developing countries such as India, various terrain hills are experiencing increasing population density and rapid infrastructural development, which may increase the vulnerability to potential landslides and thus socio-economic losses. The study area is situated in the Himalayan region, where landslides are a common occurrence due to the area's topography and geology. The region encompasses several major cities, including Darjeeling, Ghum, Rangbull, and Sonada, which attract a large number of tourists daily. The population in the area is dense, and significant land-use changes have taken place over the years, further exacerbating the risk of landslides. By conducting landslide susceptibility mapping in these areas, it is possible to identify high-risk zones and inform land-use planning decisions to mitigate the impact of landslides on the local population and infrastructure. Quantitative and qualitative approaches are used to classify landslide susceptibility mapping (LSM) [1; 2; 3; 4]. To prevent these losses and improve the efficiency of LSM, new methods and different hybrid models are introduced every day by researchers [5; 6; 7; 8; 9; 10]. Machine learning approaches have also been introduced in this field in recent years [11; 12; 13; 14]. Different ensemble methods and hybrid models are also used to assess and identify LSM [8; 15]. The overall incidences of landslides are increasing, and the factors causing the landslides are the growth of urbanization, and development in landslide-prone areas. Changing climate patterns, increased local rainfall, etc., are the reasons behind constant changes in landslide-prone zones [16; 17]. Most losses would be avoided by identifying the problematic areas, land deforestation, etc. Hence, recognizing the existing and potentially unstable slopes is most significant task. Due to mass changes in urbanization and growing awareness of its socio-economic impact in the hilly region, significant attention is focused on the study of landslides [18]. Hence, LSM of any terrain helps to classify areas into different classes concerning the degree of potential hazards. Recently, various GIS tools have helped to identify conditioning factors that may influence an area's vulnerability to landslides. Rainfall and earthquakes significantly trigger landslides due to their external and temporal aspects [19]. For effective disaster management and future improvement, developers and engineers use landslide susceptibility maps [12; 20; 21]. The identification of risk areas, coupled with a proper landslide assessment plan, can help disaster management planners and developers to minimize accidents and losses. Many parts of the Himalayas are not physically accessible but geospatially can be accessible everywhere, so assessing landslides using the geospatial technique is easy [22; 23]. Geospatial and geotechnical modeling has been used by different researchers in the last few years for landslide susceptibility and hazard assessment using different parameters [24; 25; 26]. Land-use change is a major contributor to landslides, causing significant environmental and socioeconomic impacts [27]. In order to achieve sustainability goals and reduce landslide risks, understanding the spatiotemporal evolution and influencing factors of land-use change is essential [28; 29; 30]. Land-use change can create slope instability, increasing the susceptibility of an area to landslides. Deforestation, urbanization, and mining are examples of land-use changes that can alter soil properties and destabilize slopes [31].