Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm

The quality of the sample plays a vital role in developing accurate models using machine learning. This aspect is equally important when evaluating regional landslide susceptibility using machine learning. Previous studies have mostly employed random generation methods to select samples, which often fail to select representative samples. Therefore, this study proposes the KK-sampling method, which uses K-means and KNN algorithms to analyze relevant attributes of the study area and select samples. To evaluate the effectiveness of the proposed method, this study employed MLP, RF, and XGBoost models in conjunction with the KK-sampling method, with Zhong County, Chongqing serving as a case study. The results indicate that the KK-sampling method significantly improves the stability and accuracy of the model. Additionally, this study analyzed the importance of landslide factors in Zhong County using SHAP values. The findings provide a reference for establishing a reasonable and effective landslide susceptibility model in the region.