Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment.

This study applies the Gaussian Analytical Hierarchy Process (Gaussian AHP) to landslide susceptibility mapping and demonstrates its superior methodological rigor and predictive performance relative to the traditional AHP method. Susceptibility maps produced by Gaussian AHP allocated 26.31% of the study area to the very high susceptibility class, outperforming the traditional AHP's estimated share (23.52%), and achieved a more balanced distribution across all five classes. Validation against a high resolution inventory of 97,742 landslide samples collected during the February 2023 São Sebastião event-divided into 70% training and 30% validation subsets-yielded improved metrics: ROC area under the curve of 0.6360 versus 0.6220; overall accuracy of 0.6364 versus 0.6229, balanced accuracy of 0.6356 versus 0.6221; and sensitivity of 0.3585 versus 0.3116, for the Gaussian and traditional AHP methods respectively. An uncertainty analysis quantified a 56.16% disagreement between the two methods, revealing that Gaussian AHP reduced classification ambiguity in critical classes. A complementary density-based assessment, comparing observed landslide crown points and scar polygons against susceptibility class areas, showed that Gaussian AHP produced a gradual, coherent increase in normalized landslide density from very low to very high susceptibility, whereas traditional AHP displayed sharp breaks in intermediate classes. These findings confirm that Gaussian AHP enhances objectivity, spatial coherence, and operational reliability, better aligning high density landslide clusters with the highest susceptibility zones. By leveraging statistical weighting, Gaussian AHP streamlines data preprocessing and reduces the need for expert calibration, making it well suited for assessments in data rich environments. The statistical weighting procedure facilitates the integration of diverse geospatial datasets and supports robust, reproducible multicriteria decision analysis. Its integration with accurate machine learning-derived land use/land cover data and refined climate data is recommended to further improve predictive accuracy and support proactive landslide risk management strategies. The proposed approach can additionally meet operational purposes, provided that near real-time climate data, updated geospatial databases, and massive computing resources are available.