## 1 Introduction  
Nepal ranks 11 th among the seismically most active countries in the world [1]. Most recently, in the history of strong earthquakes in the region, an earthquake of Mw 7.8 struck Barpak Village of Gorkha District in central Nepal at 11:56 local time on 25 April 2015 (often referred to as the 2015 Nepal Earthquake or Gorkha Earthquake). Nepal had not experienced an earthquake of this scale in the last eight decades [2]. The disaster claimed about 9000 people and led to complete and partial collapse of nearly 1.1 million building structures [3]. In about two years after the earthquake, more than 400 aftershocks of Mw 4.0 or greater were recorded, which collectively triggered thousands of landslides in and around the epicentral region. Co-seismic landslide damage may often surpass the damage caused by an earthquake itself. In fact, the 2015 Gorkha Earthquake exemplified this situation. The ramification of the co-seismic landslides was particularly acute in the areas surrounding Gorkha District, where about 70% of the residential properties were destroyed and a total of 443 people were killed [2]. Landslides on the Nepal Himalayan mountains are particularly devastating because they often have the potential to wipe out entire communities on top of damaging the building structures, social infrastructure, and socio-economic system [4]. Historical earthquake events, such as the 1934 Bihar-Nepal Earthquake and the 1988 Nepal Earthquake, also triggered a large number of landslides, isolating several human settlements and severely damaging the infrastructure [5]. The 2015 earthquake triggered widespread landslides that led to significant structural damage, displacement of a large number of people, and destruction of entire human settlements [6; 7]. These events underscore the limitations of conventional methods of landslide susceptibility assessment, which often fail to capture the complex interplay of the contributing factors. These limitations necessitate the use of more robust methods, such as machine learning (ML) and artificial intelligence (AI) techniques, to accurately assess and predict the susceptibility of mountain slopes to earthquake-induced landslides [8]. Landslides on Nepal mountains are particularly influenced by steep topography, young and complex geology, active thrust fault systems, ongoing mountain-building processes, and uncontrolled anthropogenic activities [9; 10]. While much of the landslide research focuses on rain-induced landslides, earthquake-induced landslides remain underexplored. Recent studies (e.g., [11; 12], etc.) have applied AI and statistical methods for landslide susceptibility analysis because of the ease of handling large datasets and capturing complex, non-linear relationships. These methods can extract meaningful patterns from historical data and are vital for anticipating future landslide occurrences. For instance, Logistic Regression (LR) models were used in the examination of landslide susceptibility by [PERSON] et al. [13], while [PERSON] et al. [14] applied the Random Forest (RF) model and [PERSON] et al. [15] employed the eXtreme Gradient Boosting (XGBoost) algorithm. The research carried out by [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18] demonstrates the superior performance of ML models compared to traditional statistical or heuristic methods. However, ML models may still struggle when dealing with uncertainties in the predictor variables. Recent studies have increasingly emphasized the importance of integrating advanced modeling techniques and detailed field investigations to enhance the understanding of landslide mechanisms and susceptibility. For instance, [PERSON] et al. [19] analyzed a medium-sized landslide in Zhenxiong, Yunnan, China, which led to a major disaster, and highlighted how such moderate-scale events can cause significant damage due to complex failure processes and dynamic motion characteristics. Similarly, [PERSON] et al.