Machine learning is widely employed in landslide susceptibility assessment (LSA). To address the issues of weak classification ability of individual machine learning models and unknown effectiveness of various machine learning models in different study areas, we propose a three-level stacking ensemble strategy for LSA. This study focuses on the eastern part of Enshi City, Hubei Province, with 676 historical landslide points within the area as samples. Twelve landslide conditioning factors (LCFs) related to terrain, geology, hydrology, and human activities were selected. Models were constructed using seven basic machine learning classifiers to obtain the landslide susceptibility index of the study area and to generate landslide susceptibility maps. Subsequently, heterogeneous ensemble strategies, including voting, stacking, and three-level stacking (3LStacking), were applied to update the maps. We utilized the area under the receiver operating characteristic (ROC) curve (AUC) and mean squared error (MSE) as evaluation metrics. The results indicate that the heterogeneous ensemble strategy outperforms the basic classifiers. Among them, the proposed method achieved the highest accuracy, with an AUC value of 0.950 and an MSE of 0.058. This suggests that the 3LStacking significantly enhances the performance of machine learning modeling and is a reliable method for LSA. The findings of this study will contribute to improving the accuracy of regional LSA. Additionally, through an analysis of the importance of LCFs in the basic classifiers, it was found that distance to roads and elevation are critical triggering factors for landslides in the study area, while slope, distance to streams, and distance to faults also have different degrees of influence on landslide development.
Liu et al. (Sun,) studied this question.