GIS-based modeling of landslide susceptibility zonation by integrating the frequency ratio and objective–subjective weighting approach: a case study in a tropical monsoon climate region

Accurate detection of landslide spatial patterns is vital in susceptibility, hazard, and risk disaster mapping. Geographic Information System (GIS)-based quantitative approaches provide a rigorous procedure for gaining deep insight into natural anthropogenic landslides from different scales. This study aims to implement comprehensive solution retrieving the susceptibility index. For that purpose, inventory was performed tropical monsoon climate region, with magnitude elevation spanning −65 m 1,900 above sea, considering 15 fundamental causative factors belonging groups topography, hydrology, geology, land cover conditions activities, weather. The frequency ratio (FR) implemented rank subclasses each factor. factor weight estimation, were applied, including subjective-based analytic hierarchy process (AHP), objective-based Shannon entropy (SE), synergy both methods (AHP–SE), built on these two approaches. Out 271 identified locations, 70% (196 points) used training remaining 30% (71 applied validation. results showed integrated AHP–SE outperformed individual approaches, area under receiver operating characteristic curve (AUC) reaching 0.876, following SE (AUC = 0.848) AHP 0.818). In approach, pattern monsoons confirmed as most crucial landslide-predisposing research contributes novel discussion by integrating knowledge-based consultation statistical data analysis accurate geospatial data, incorporating significant explanatory toward reliable landslide-prone zonation over space time dimensions.