Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Abstract

Landslides, recognized as a significant global natural disaster, necessitate an exploration of the impact of various resolution types in sampling strategies on Landslide Susceptibility Mapping (LSM) results. This study focuses on the segment from Zigui to Badong within the Three Gorges Reservoir Area, utilizing two resolution types: sampling resolution and spatial resolution, The Support Vector Machine (SVM) is employed to obtain LSM results, which are then analyzed using Receiver Operating Characteristic (ROC) curve, specific category accuracy and statistical methods. Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used to verify the reliability of the results. Additionally, five common machine learning models, including Logistic Regression (LR), are used to conduct experiments on four sampling resolutions (10 m,30 m,50 m and 70 m) to further investigate the effect of sampling resolution on LSM results. These are evaluated using a comprehensive quantitative method. The results reveal that increasing spatial resolution improves the prediction accuracy, while increasing sampling resolution produces a contrary effect. Furthermore, the impact of spatial resolution on LSM results is more pronounced than that of sampling resolution. Finally, Fanjiaping landslide and Huangtupo landslide are selected as references for comparative analysis, with the results aligning with engineering reality.