Landslide Susceptibility Mapping Optimization for Improved Risk Assessment Using Multicollinearity Analysis and Machine Learning Technique

This study integrates geospatial modeling with multi-criteria decision analysis for an improved approach to landslide susceptibility mapping (LSM). This approach addresses key challenges in LSM through sophisticated multicollinearity analysis and machine learning strategies. We compared three machine learning models for weighting, and of them the Permutation-Weighted model yielded the best prediction results, with an Area Under Curve (AUC) of 95%, an accuracy of 69%, and a recall of 66%. To resolve perfect multicollinearity (r = 1) between land use land cover (LULC) and geological factors, we implemented Principal Component Analysis (PCA). The selected factors demonstrated strong predictive power, with the PCA-derived features exhibiting the best performance, having a Variation Inflation Factor (VIF) of 1.004. Slope appeared as the most influential factor (51.7% contribution), while the Topographic Wetness Index (TWI) was less dominant with only 6.6%. Multiple landslide susceptibility mapping methods yielded consistent results, with 29.8–30.1% of the study area showing moderate susceptibility and 35.2–36.9% in the high to very high susceptibility class. The model also incorporated vulnerability parameters weighted by the United Nations Office for Disaster Risk Reduction (UNDRR) indicators, including farmland, buildings, bare land, water bodies, roads, and amenities to generate hazard, vulnerability, and risk maps. The results were verified through visual comparison with high-resolution Google Earth imagery. The Permutation-Weighted model performed better than others, categorizing 12.4% at high-risk, while Random Forest (RF) categorized 7.2% at high risk. This study makes three key contributions: (1) It establishes the effectiveness of PCA/VIF for variable selection, (2) it provides a comparison of machine learning weighting techniques, and (3) it validates a workflow applicable to data-scarce regions.