Mapping of landslide potential in Pyeongchang-gun, South Korea, using machine learning meta-based optimization algorithms

Abstract

Landslides are geological hazards that can have severe impacts, threatening both the people and the local environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting landslides and mitigating landslide-associated damage in areas prone to these events. This study aims to investigate the combination of using an adaptive network-based fuzzy inference system (ANFIS) with metaheuristic optimization algorithms: gray wolf optimizer (GWO), particle swarm optimization algorithm (PSO), and the imperialist competitive algorithm (ICA) in mapping landslide potential. The study area was Pyeongchang-gun, South Korea, for which an accurate landslide inventory dataset is available. A landslide inventory map was organized, and the data were separated randomly into training data (70%) and validation data (30%). In addition, 16 landslide-related factors consisting of geo-environmental and topo-hydrological factors were considered as predictive variables. This landslide susceptibility model was be evaluated based on the value of the area under the receiver operating characteristic (ROC) curve (AUC) to measure its accuracy. Based on the maps, the validation results showed that the optimized models of ANFIS-ICA, ANFIS-PSO, and ANFIS-GWO had AUC accuracies of 0.927, 0.947, and 0.968, respectively. The result from the hybrid algorithms model of ANFIS with metaheuristic algorithms outperformed the standalone ANFIS model in terms of accuracy in predicting landslide potential. Therefore, the ML algorithm and optimization algorithm models proposed in this study are more suitable for landslide susceptibility mapping in the study area.