## 1 Introduction  
Landslides are one of the most common geological disasters and are characterized by their diverse types, large scale, frequent occurrences, and widespread distribution. These disasters significantly impact human production, livelihoods, and economic development [1]. With the development of the economy and increasing human activities disturbing the natural environment, geological disasters are occurring more frequently. According to data from the World Bank, approximately 3.7 million square kilometers of inland areas on Earth are prone to landslides. This poses a significant threat to the lives and property security of people in disaster-prone areas. People are also becoming increasingly aware of the importance of disaster assessment and prevention [2]. Therefore, there is an urgent need for an effective means of reducing the disasters caused by landslides. Currently, the most effective method believed to reduce landslide risk is through the reliable detection, assessment, and identification of landslide-prone areas [3]. In this context, landslide susceptibility assessment has become a necessary means to reduce disasters [4]. Significant progress has been made in landslide susceptibility research, both domestically and internationally. Researchers employ a diverse range of data sources, such as remote sensing images, geological survey data, meteorological data, and topographical data, to create multi-dimensional assessment models for evaluating landslide susceptibility. These frameworks typically consider factors such as terrain slope, geological structure, soil type, vegetation cover, rainfall, and human activities. By comprehensively analyzing these factors, researchers can assess the potential risk of landslides, providing a scientific basis for disaster prevention. Landslide susceptibility assessment can provide valuable insights regarding various aspects such as regional landslide disaster management, community-based monitoring, and prevention, as well as urban planning and construction. Therefore, such assessments demonstrate significant value [5]. Landslide susceptibility assessment aims to evaluate the spatial probability of landslide occurrence in a given area, with the goal of predicting high-risk zones where landslides may occur under specified environmental conditions [6]. Landslide susceptibility assessment focuses on identifying potential locations where landslides may occur in the future in a target area, without considering when or how frequently they may occur. Data-driven methods, including machine learning algorithms, are often used to calculate landslide susceptibility, which refers to the \"likelihood of landslides occurring in a given area\". Research on landslide susceptibility assessment began in the early 1970s. In 1972, [PERSON] et al. [7] produced a landslide susceptibility map of San Mateo County, California, USA. During this period, landslide susceptibility assessment primarily relied on expert experience and subjective judgment for qualitative evaluation. Following this initial research, the concept of landslide susceptibility assessment emerged and gained widespread attention [8]. Since the 1980s, with the deepening of evaluation theories and the improvement of technical methods, geological hazard assessment has evolved from its initial qualitative analysis to today's quantitative analysis [9]. After the 1990s, with the development of GIS (geographic information systems) technology, the unique spatial data storage, spatial analysis capabilities, and visualization capabilities of GIS have provided novel means for landslide susceptibility assessment [10]. With the development of computer technology and statistics, machine learning methods have been applied to landslide susceptibility assessment. Due to the powerful nonlinear processing capabilities of machine learning, its application has become a current research hotspot.