## 5 Discussion  
Landslide susceptibility mapping (LSM) is of great significance in the landslide-prone areas and it is challenging to create LSM with high accuracy and reliability. Firstly, it is usually not so easy to collect and preprocess enough multi-source data from historical data. Secondly, it is critical to select and determine appropriate landslide predisposing factors (LPFs) to establish a landslide susceptibility assessment system. There are numerous LPFs, varying with geographical and geological features locally and globally. [PERSON] et al. [7] combined AHP and IV methods in GIS to analyze eight landslide factors (e.g., slope, aspect, distance to faults/drainage, lithology) and assess landslide susceptibility for a highway in Constantine, northeastern Algeria. [PERSON] et al. [18] selected six factors (e.g., slope, terrain ruggedness, rock type, proximity to geological contacts/drainage, land use) and applied the Weight of Evidence model to assess landslide susceptibility across Zagreb, Croatia. [PERSON] et al. [20] examined the relationship between landslides and four factors (lithology, slope, aspect, land use) in the Fiumarella di Corleto catchment, southern Apennines, Italy. There is a lack of standardized criteria for model factor selection up to now. In this study, 16 LPFs were selected through analysis of multicollinearity and of the contributing conditions in the study area by integrating GIS with machine learning.  
Excluding the 10-day rainfall factor, the three base models--Random Forest, XGBost, and CatBoost--as well as the ensemble model demonstrated strong performance in landslide prediction, achieving AUC values of 0.8841, 0.8572, 0.8787, and 0.8784, respectively, when 15 influencing factors were used for evaluation. From the table of importance coefficients of landslide-influencing factors obtained through the ensemble model, it can be observed that elevation ranked first in all models, while NDVI ranked second in all models except XGBoost. The ensemble model, which integrates the predictions of the three base learners, revealed the following importance coefficients for the top seven influencing factors: elevation (15.5971%), NDVI (11.6825%), stratigraphic types (DSN, 7.8708%), slope (7.4366%), rock texture (YSC), 6.5887%), plan curvature (6.4489%), and distance to faults (6.1751%).  
After introducing the 10-day rainfall factor, 16 influencing factors were evaluated. The AUC values of the three base models and the ensemble model improved significantly. The ranking of factor importance also changed, with the top seven factors being:10-day rainfall (48.9018%), distance to rivers (7.703%), stratigraphic types (7.6972%), elevation (5.7843%), NDVI (5.0007%), distance to faults (4.0647%), and slope (3.5335%). A comparison of the results before and after the introduction of ten-day rainfall factor reveals that stratigraphic types, elevation, and NDVI remained among the top five most important factors in both scenarios. Notably, distance to rivers, which originally ranked 15 th, rose to become the second most critical factor in landslide risk assessment after the introduction of rainfall. This highlights the cross-impact and interaction among the predisposing factors. Additionally, rainfall is a dynamic variable, and determining the threshold for the rainfall contributing to landslides is highly challenging. This may lead to uncertainty in landslide prediction. Overcoming the spatiotemporal scale mismatch between dynamic meteorological/hydrological factors and static geological parameters remains a critical challenge. Blending is an ensemble model method that improves overall predictive performance by combining the predictions of multiple base models to help analyze the relationship between various geographical factors and landslide occurrence, as well as estimate the probability of such events.