Comparison study of a landslide-event hazard mapping using a multi-approach of fuzzy logic, TRIGRS model, and support vector machine in a data-scarce Andes Mountain region

Abstract

Landslides are a significant global hazard, especially prevalent in regions with high rainfall, active tectonic processes, and rugged topography, such as the Colombian Andean region. Therefore, it is crucial to identify areas prone to landslides in order to protect human lives and mitigate the adverse impacts on national economies, especially in developing countries situated in tropical and mountainous regions. Assessing landslide hazard and susceptibility is a fundamental step in comprehending the fundamental characteristics of slopes susceptible to failure, particularly under extreme rainfall conditions. Various researchers have devised methods and techniques to assess and map landslides, employing heuristic, statistical, and deterministic approaches. This study carried out a geographic information system-based approach for shallow landslides, with the objective to compare different methods for a landslide-event hazard mapping using the landslide records on May 18, 2015, triggered by a rainstorm in the La Liboriana basin (Colombia). In the first place, a fuzzy logic gamma model was applied using landslide conditioning factors. Then, the deterministic model TRIGRS was applied to assess shallow landslides. Finally, a support vector machine (SVM) model was used to obtain an intermediate scale solution. All models consider the rainfall that triggered the aforementioned landslide event. The results indicated that the SVM (radial basis function) model permits to obtain a better performance (AUC = 0.95) in landslide hazard zonation rather than quantitative heuristic fuzzy gamma model (AUC = 0.86) and the deterministic TRIGRS model (AUC = 0.60), obtaining best accurate at predicting the landslide hazard in the study area.