## 1 Introduction  
As a highly destructive natural disaster, landslides often cause severe damage to vegetation, infrastructure, and human life and property [1]. Due to the sudden onset of landslides and the difficulty in issuing timely warnings, they are considered to be among the most threatening natural disasters in mountainous areas [2]. Landslide refers to the downward movement of large amounts of rock, debris, or soil along a slope under the influence of gravity [3]. Landslide susceptibility prediction (LSP) is a core tool for assessing landslide risk and formulating disaster prevention and mitigation measures [4]. LSP evaluates the likelihood of landslides in a specific area based on terrain, environmental features, and other conditions [5]. By integrating various conditioning factors such as topography, geology, and hydrology, LSP study can effectively identify high-susceptibility areas, providing scientific guidance for land use planning and disaster prevention [6]. [PERSON] was one of the first to apply statistical methods to explore the relationships among morphometric measurements, geomechanics, rock characteristics, structural features, and the stability of 250 slopes in Western Germany [7]. Several methods and approaches have been proposed to assess landslide susceptibility, including but not limited to geomorphological mapping, landslide inventory analysis, heuristic terrain zoning,physically based numerical modelling, and statistical classification techniques [8]. Geographical mapping relies on expert evaluation to identify and map slope instability conditions, with the quality depending on the investigator's expertise and the study area's complexity [9]. Landslide inventory analysis predicts future spatial occurrences based on past and present landslide distributions, often using landslide density maps. Heuristic approaches rank and weight instability factors based on assumed importance; however, their quality relies on the investigator's understanding of the causes [10]. Physically based landslide susceptibility assessment methods are based on the numerical modeling of slope failure processes [6], which are often based on the infinite slope model, which assumes a planar rupture surface parallel to the topographic surface, usually corresponding to the bedrock/hillslope deposit discontinuity [11], while statistical methods explore the relationship between instability factors and landslide distribution, often employing regression or probabilistic models [12]. These traditional methods have been enhanced by advancements in GISs [13], remote sensing, and computational power, with machine learning now offering a powerful tool for more accurate and scalable landslide susceptibility modeling. With advancements in remote sensing and computational power, more researchers are adopting machine learning methods for LSP [14]. A literature review indicates that around 15.8% of LSP studies utilize machine learning as the primary modeling approach [15]. Widely used algorithms include decision trees, support vector machines (SVMs), neural networks, and Random Forest. Each of these machine learning methods has unique advantages in LSP studies. Decision tree (DT) models are the most used in LSP studies. However, DT models struggle with complex non-linear relationships and are prone to overfitting. Moreover, slight variations in input data can significantly affect the structure of the DT, making it sensitive to data fluctuations [16; 17]. Support vector machines (SVMs) offer slightly higher accuracy in shallow landslide assessments than other algorithms [18]. However, SVM models have high computational complexity and require careful parameter tuning. Neural networks (NNs) and deep learning (DL) excel at automatic feature extraction and are particularly effective in processing large datasets. However, both methods often encounter generalization issues, with underfitting and overfitting negatively impacting model performance [19].