## 1 Introduction  
Landslides, one of the most devastating geological disasters, occur frequently under the effect of climate change in mountainous areas of China and even the world [1,2], causing heavy casualties, economic losses, and severe damage to the ecological environment. Hunan Province stands out as an area prone to landslides, with 2561 landslides occurring in 2022 [3]. According to historical records from the Department of Emergency Management of Hunan Province, China, from 2015 to 2018, Anhua county witnessed a total of 434 landslides with the scales ranging from 10 to 969,620 m\({}^{3}\), inflicting a direct economic loss of about \(\Psi\)81,730,000 [4]. This study aims to conduct susceptibility analysis of landslides and explore rainfall-induced landslide vulnerable areas in Yiyang city to provide reference for local landslide warning and control. Comprehensive consideration of the contribution of various factors is needed to assess landslide susceptibility. Due to the uncertainty of time and space of landslide occurrence, it is still a difficult task to predict landslide-prone areas and related hazards [5]. Landslide susceptibility analysis can be made by qualitative analysis and quantitative analysis. The qualitative method is mainly based on geomorphology and the heuristic method [6], and the weight of each susceptibility variable is determined by professional investigators based on actual conditions and their expertise in predicting potential slope failures [7]. The quantitative analysis approach for assessing landslides is to create the landslide susceptibility map of a certain area to predict the landslide probability based on the predisposing factors and numerical expressions of landslides [8]. The existing quantitative analysis methods can be divided into geotechnical engineering methods [9; 10] and statistical methods [11; 12]. Landslide prediction based on geotechnical engineering methods relies mainly on the application of slope stability theory and principles, such as limit equilibrium and the finite element method [13], requiring high accuracy of geomechanical parameters and hydrologic and other parameters and assuming that they are evenly distributed in the entire study area. Therefore, such methods are only suitable for conducting limited-range studies on landslide prediction [14]. Compared with geotechnical engineering approaches, statistics-based landslide prediction methods necessitate a larger dataset, rendering them suitable for the prediction of large-scale landslides [13]. The most representative is the landslide prediction method based on machine learning algorithms, which has been widely used in landslide early warning analysis in recent years. [PERSON] et al. [15] proposed a methodology for analyzing landslide susceptibility and its driving factors based on an FR-weighted GeoDetector. [PERSON] and [PERSON] [16] conducted the modeling of landslide susceptibility by using XGBoost models. [PERSON] et al. [17] utilized random forests to construct a landslide susceptibility map. [PERSON] et al. [6] employed a hybrid machine learning approach based on Credal decision trees (CDT) to assess the susceptibility of landslides in the Taleghan-Alamut basin of Iran. [PERSON] et al. [18] conducted a large-scale susceptibility assessment of landslides in the city of Zagreb, Croatia, by employing an evidence weight model. [PERSON] et al. [19] prepared landslide susceptibility mapping (LSM) using remote sensing thematic layers and adopted a multi-criteria decision analysis (MCDA) model analytical hierarchy process (AHP) to identify landslide-prone zones in one part of Darjeeling, India. As a powerful tool, GIS provides data processing, spatial analysis, and visualization functions for landslide susceptibility mapping, thus improving the accuracy and efficiency of landslide susceptibility assessment. [PERSON] et al.