AUTHOR=Hoa Pham Viet , Tuan Nguyen Quang , Hong Pham Viet , Thao Giang Thi Phuong , Binh Nguyen An 

TITLE=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

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1175567

DOI=10.3389/fenvs.2023.1175567

ISSN=2296-665X

ABSTRACT=Accurate detection of landslide spatial patterns is vital in susceptibility, hazard, and risk disaster mapping. GIS-based quantitative approaches provide a rigorous procedure for deep insight into natural and anthropogenic landslides from different scales. This study aims to implement a comprehensive solution for retrieving the landslide susceptibility index. For that purpose, landslide inventory was performed in a tropical monsoon climate region with a magnitude of elevation spanning from -65 m to 1900 m above the sea, considering fifteen fundamental causative factors belonging to the groups of topography, hydrology, geology, natural conditions and anthropogenic activities, and weather. The frequency ratio (FR) was implemented to rank subclasses in each causative factor. For factors weight estimation, different approaches were applied, including subjective-based Analytic Hierarchy Process (AHP), objective-based Shannon Entropy (SE), as well as the synergy of both methods (AHP-SE) built on these two approaches. Landslide susceptibility maps built on 70% of landslide inventory points following proposed procedures were evaluated against the remaining 30% of the data. Results showed that AHP-SE outperformed the two individual approaches, with Area Under Receiver Operation Characteristic Curve (AUC) reaching 0.876. In the synergy approach, the climate pattern under tropical monsoons was confirmed as the most crucial landslide predisposing factor. The research contributes to a novel discussion by integrating knowledge-based consultation and statistical data analysis of accurate geospatial data, incorporating significant explanatory factors towards a reliable landslide-prone zonation over space and time dimensions.