# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 2 Geological Background
### Landslide Inventory and Influencing Factors  
A detailed and accurate landslide inventory is essential for conducting landslide susceptibility mapping. First, it provides comprehensive spatio-temporal information about historical landslide events, enhancing the accuracy of regional historical data. This enables researchers to better understand landslide patterns, characteristics, and frequency, as well as their triggering factors, laying a solid foundation for regional landslide risk assessments. Second, landslide inventory data are fundamental for constructing susceptibility and prediction models. By analyzing historical data, researchers can identify the spatial distribution characteristics of landslides, develop precise susceptibility models, and improve the accuracy and reliability of landslide predictions. Finally, an accurate landslide inventory plays a key role in guiding mitigation measures. Through systematic data collection and field surveys, this study established a comprehensive landslide inventory for the period 1968-2022, documenting 2028 historical  
Figure 1: Overview of the study area. events within the study area. The temporal analysis reveals a marked increase in landslide frequency between 1998 and 2010 (Figure 2a), closely associated with the Three Gorges Reservoir impoundment project. Following the reservoir's initial filling in 2003, accelerated water-level fluctuations induced substantial alterations to the bank stress distributions and hydrodynamic conditions, precipitating widespread slope failures. As shown in Figure 2b, the majority of landslides cover an area of 0 to 50 \(\times\) 104 m\({}^{2}\), while a small proportion exceeds 100 \(\times\) 10\({}^{4}\) m\({}^{2}\). Regarding landslide volume, most landslides range from 0 to 500 \(\times\) 10\({}^{4}\) m\({}^{3}\), with a few outliers reaching over 1000 \(\times\) 10\({}^{4}\) m\({}^{3}\), and some even surpassing 1500 \(\times\) 10\({}^{4}\) m\({}^{3}\) (Figure 2c), highlighting the massive scale and high risks posed by these events. The primary triggering factors identified include rainfall and fluctuations in reservoir water levels, along with substantial contributions from human activities, such as artificial slope cutting and loading. In regard to landslide susceptibility modeling, the selection and comprehensive analysis of influencing factors are critical in regard to the accuracy and effectiveness of the model. These factors encompass various dimensions, including topography, geology, hydrology, soil characteristics, vegetation cover, and human activities, each offering distinct contributions to the preconditioning of slope instability. By offering detailed descriptions of the geographical and environmental conditions, these factors help researchers identify and quantify the key factors that influence slope failure. Integrating multi-source data into landslide susceptibility models enables the simulation and prediction of landslide probabilities within specific regions. Based on literature reviews [37; 38; 39] and data availability, this study considers the DEM, slope, plan curvature, profile curvature, the Stream Power Index (SPI), the Topographic Wetness Index (TWI), lithology, soil type, distance to the road, distance to the river, land  
Figure 2: **(a)** Landslide occurrences per year in the study area, (**b**) distribution of landslide areas in the study area, and (**c**) distribution of landslide volumes in the study area. use, and the Normalized Difference Vegetation Index (NDVI) (Figure 3). The DEM, slope, plan curvature, and profile curvature data are derived from the ALOS Digital Elevation Model, provided by the Alaska Satellite Facility, with a spatial resolution of 12.5 m, in raster format (Table 1). The DEM serves as a fundamental topographic parameter in landslide analysis, governing precipitation accumulation and surface water flow dynamics. Slope, a key factor of mechanical stability, directly correlates with landslide risk; steeper slopes generally exhibit higher susceptibility.