GIS-Based Multi-Criteria Evaluation (MCE) for Landslide Susceptibility Mapping: A Review

This Landslides are among the most destructive natural hazards, posing significant risks to human life, infrastructure, and the environment. Accurate landslide susceptibility mapping (LSM) is essential for effective disaster risk reduction and land-use planning. This review paper explores the application of Geographic Information System (GIS)-based Multi-Criteria Evaluation (MCE) methods in landslide susceptibility mapping. It synthesizes current methodologies, evaluates the effectiveness of various decision-making techniques—such as the Analytical Hierarchy Process (AHP), Weighted Linear Combination (WLC), and fuzzy logic—and highlights their integration with spatial data layers representing key landslide conditioning factors (e.g., slope, soil type, rainfall, land use, geology). The review also addresses challenges related to data quality, subjective weighting, and model validation, while discussing advancements in machine learning integration and hybrid approaches. Overall, this paper provides a comprehensive overview of the strengths and limitations of MCE-based LSM frameworks, offering recommendations for future improvements in predictive accuracy and practical implementations.