## 1 Introduction  
Landslides occur worldwide every year at different scales. According to the NASA Goddard Space Flight Center's Landslide Catalog, there were 15,455 landslide events worldwide between 1950 and 2022, causing over 59,862 deaths [1]. Due to the unique combination of geology and climate, China is highly prone to landslides [2]. In recent years, landslides have been responsible for as many as 70% of all geological disasters in China [3]. In particular, due to its major mountainous and hilly landform, as well as precipitation and topography, in Zhejiang Province, the occurrence of slope instability events is greatly enhanced. This underscores the importance of early identification and preventive measures to mitigate the severe effects of landslides on human lives and property. Shallow landslide susceptibility research areas can be summarized into four broad categories: (1) The first is process-based prediction models of landslide instability mechanisms and physical processes [4; 5]. These studies are founded on principles with respect to geology, soil mechanics, fluid dynamics, and geomechanics [6]. They consider precipitation infiltration [7], groundwater flow, and alterations in the strength of soil [8] by simulating and hypothesizing the ways in which landslide occurrence takes place. Additionally, current physics-based methods for predicting rainfall-induced shallow landslides rely on physical equations and principles from physics. These methods require only a limited set of input data, which is typically easy to obtain, making them highly suitable for rapid assessments in data-scarce regions. (2) The second type is physics-based models of landslide susceptibility prediction. These models rely on physics-based equations and principles to predict landslide occurrence. They require only a limited set of input data, which is typically easy to obtain, making them highly suitable for quick assessments in regions where data are scarce [9; 10; 11]. However, although these physical models can accurately predict the timing of landslides, especially for short-term monitoring and early warning [12], they are usually limited to small areas or single events [13]. Moreover, while they may not offer high-resolution predictions, their simplicity and the ease with which they can be applied make them valuable tools for rapid landslide risk assessments in many areas. (3) The third type comprises landslide susceptibility mapping, which is achieved using geographic information system (GIS) and remote sensing technologies. Geographic data relating to terrain, geology, precipitation, and land use are integrated to recognize areas that may turn out to be susceptible to landslides [14; 15; 16]. These models are mostly used for hazard zoning at macroscopic scales [17], as well as for identifying high-risk areas [18], incorporating ground observations to increase the accuracy of the results [19]. (4) The fourth type includes building landslide susceptibility assessment models that are then applied to historical landslide datasets [20]. These models use past landslide events to create a regression model or any other predictive model that can be used to evaluate susceptibility [21]. For example, precipitation threshold models, which relate rainfall intensity and duration to landslide occurrence [22], are widely used in shallow landslide disaster prediction research [23; 24; 25]. These methods are based on past events, have the advantages of simplicity and efficiency, and have been found to provide quick identification of probable landslide conditions. There are three common approaches for developing predictive models using historical landslide events: (1) The first approach is expert assessment methods [26; 27; 28], which require expert knowledge for the analysis of landslide likelihood and severity; however, these methods are subjective and produce variable results.