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

Abstract

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 and anthropogenic landslides from different scales. This study aims to implement a comprehensive solution for retrieving the landslide susceptibility index. For that purpose, a landslide inventory was performed in a tropical monsoon climate region, with a magnitude of elevation spanning from −65 m to 1,900 m above the sea, considering 15 fundamental causative factors belonging to the groups of topography, hydrology, geology, land cover conditions and anthropogenic activities, and weather. The frequency ratio (FR) was implemented to rank subclasses in each causative factor. For factor weight estimation, different approaches were applied, including the subjective-based analytic hierarchy process (AHP), objective-based Shannon entropy (SE), and a synergy of both methods (AHP–SE), built on these two approaches. Out of the 271 identified landslide locations, 70% (196 points) were used for training and the remaining 30% (71 points) were applied for validation. The results showed that the integrated AHP–SE outperformed the two individual approaches, with the area under the receiver operating characteristic curve (AUC) reaching 0.876, following SE (AUC = 0.848) and AHP (AUC = 0.818). 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 toward a reliable landslide-prone zonation over space and time dimensions.

Combined Methodology for Rockfall Susceptibility Mapping Using UAV Imagery Data

“…The outcome of the search presented maps of potentially unstable rocks. The delineation of rockfall sou areas was based on geomorphometric indices derived from the digital elevation mod The reliability of the method was approved by many other researchers [18,[31][32][33][34]. Mo over, monitoring observations from past data acquisition supports the veracity of the o tained results.…”

“…There are a number of approaches for rockfall susceptibility and risk detection using remote sensing survey techniques [17]. However, there is no prevalent methodology providing the best modelling due to uncertainties in predicting hazardous exogenous geological processes and the diversity of contributing factors in particular conditions [18,19]. Researchers continue to compare the effectiveness of different methods and susceptibility models.…”

“…GIS-based landslide susceptibility mapping for estimation areas where landslides may occur in the future is a well-researched approach and widely covered in articles [18,23,24]. Contributing factors are usually presented by altitude, slope, aspect, terrain ruggedness, other morphometric parameters, land cover, land use, lithology etc.…”

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

“…Landslide susceptibility models may be developed using several approaches, including multicriteria decision-making methods based on expert opinions, statistical methods, and machine learning models. Several commonly employed methods for multi-criteria decision-making and statistical methods include Multi-Criteria Decision-Making (MCDM) (Konurhan et al, 2023;Lyu & Yin, 2023;Ramiz et al, 2023;Saha et al, 2023), Analytical Hierarchy Process (AHP) (Jari et al, 2023), Evidential Belief Function (EBF) (Roy et al, 2023), Shannon Entropy (SE) (Addis, 2023;Hoa et al, 2023). However, Machine Learning (ML) and Deep Learning (DL) algorithms have been greatly employed in recent years to construct landslide susceptibility models.…”