The Influence of Non-Landslide Sample Selection Methods on Landslide Susceptibility Prediction

Abstract

Non-landslide sample selection is critical in landslide susceptibility modeling due to its direct impact on model accuracy and reliability. This study compares three sample selection strategies: whole-region random selection, landslide buffer zone selection, and the enhanced information value (EIV) method. By integrating these methods with the random forest (RF) algorithm, three models—random-RF, buffer zone-RF, and EIV-RF—were developed and evaluated. Using Henan Province as a case study, 20 environmental factors and 1021 landslide records were analyzed. The EIV method leverages machine learning to assign adaptive weights to influencing factors, prioritizing sample selection in low-susceptibility regions and avoiding high-susceptibility areas, thereby enhancing sample quality. Among the models, EIV-RF achieved the highest performance, with an AUC of 0.93, an accuracy of 85.31%, and a Kappa coefficient of 0.74. Additionally, the EIV method identified smaller, more concentrated high-susceptibility zones, covering 87.37% of historical landslide points, compared to the larger, less precise zones predicted by other methods. This study highlights the effectiveness of the EIV method in refining non-landslide sample selection and improving landslide susceptibility prediction, providing valuable insights for disaster risk reduction and land use planning.