Heterogeneous Ensemble Landslide Susceptibility Assessment Method Considering Spatial Heterogeneity

Landslide susceptibility mapping (LSM) is an effective means of assessing landslide risk and has been widely applied. However, current landslide susceptibility assessment studies have not fully considered the spatial heterogeneity characteristics between landslide assessment factors. The performance of a single model is limited by the structural characteristics of the model itself, and there is a significant limitation on the space for performance improvement. Based on these issues, this paper proposes a heterogeneous ensemble landslide susceptibility assessment method considering spatial heterogeneity. This method first combines the frequency ratio (FR), geographically weighted regression model (GWR), and clustering to partition the study area. Then, Geodetector is used to select the dominant factors for each subregion. Random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) are selected as the base models, and logistic regression (LR) is selected as the metamodel. The stacking ensemble strategy is used to construct the model to complete a landslide susceptibility assessment in Fujian Province. The results show that compared with other methods, the GWR-S-Geo model considering spatial heterogeneity proposed in this study performs best in the evaluation effect, and performance is improved by 3.2% compared with the stacking ensemble model. This study provides a certain reference value for exploration of the spatial heterogeneity of landslide susceptibility, and also provides a scientific basis for the prevention and control of landslide disasters in Fujian Province.