An ensemble learning approach for landslide susceptibility assessment considering spatial heterogeneity partitioning and feature selection

Abstract

Traditional landslide susceptibility assessment (LSA) methods typically adopt a global modeling strategy, which struggles to account for the pronounced spatial heterogeneity arising from variations in topography, geology, and vegetation conditions within a region. Furthermore, model predictive performance is often undermined by feature redundancy. To address these limitations, this study focuses on the landslide disaster early-warning demonstration area in Honghe Prefecture, Yunnan Province. It proposes an ensemble learning model termed heterogeneity feature optimized stacking (HF-stacking), which integrates spatial heterogeneity partitioning (SHP) with feature selection to improve the scientific rigor of LSA. This method initially establishes an LSA system comprising 15 static landslide conditioning factors (LCFs) and two dynamic factors representing the average annual deformation rates derived from interferometric synthetic aperture radar (InSAR) technology. Based on landslide inventory data, an SHP method combining t-distributed stochastic neighbor embedding (t-SNE) and iterative self-organizing (ISO) clustering was developed to divide the study area into subregions. Within each subregion, a tailored feature selection strategy was applied to determine the optimal feature subset. The final LSA was performed using the stacking ensemble learning approach. The results show that the HF-stacking model achieved the best overall performance, with an average AUC of 95.90% across subregions, 4.23% higher than the traditional stacking model. Other evaluation metrics also demonstrated comprehensive improvements. This study confirms that constructing an SHP framework and implementing feature selection strategies can effectively reduce the impact of spatial heterogeneity and feature redundancy, thereby significantly enhancing the predictive performance of LSA models. The proposed method contributes to improving the reliability of regional landslide risk assessments.