Machine learning-based rainfall-induced landslide susceptibility model and short-term early warning assessment in South Korea

Abstract The extreme rainfall events associated with climate change trigger landslides. Approximately 60% of South Korea comprises mountainous terrain, with steep slopes rendering it particularly prone to landslides. Despite the implementation of early warning systems by the Korea Forest Service (KFS), landslide damage remains substantial, with approximately 2345 hectares affected over the past 5 years, resulting in severe human and economic costs. The current 24-h early warning system, based on Tier 3 administrative division (Town), faces challenges in accurately identifying high-susceptibility landslide areas. Thus, a daily landslide susceptibility model that integrates landslide-associated conditioning factors with meteorological, topographic, and environmental data was designed to assess landslide susceptibility with a spatial resolution of 100 m. Using AutoML, we identified Random Forest as the optimal model for predicting landslide susceptibility. Training the model with landslide data from 2016 to 2022 resulted in an accuracy of 0.93, AUC of 0.98, and F- 1 score of 0.98. A kappa value of 0.85 indicated the effective classification of past landslides using testing data. Location-based validation using 2023 occurrences revealed highly susceptible classifications for 88% of 43 landslides, while spatial scale-based hazard assessment using observed data indicated high hazard for 96% of 607 landslides in Tiers 3 and 4 (Township). Weather forecasting was also found to affect accuracy, with 76% accuracy for forecasts made at 5:00 PM and 41% for forecasts made at 8:00 AM. It was confirmed that further calibration of forecasting data can enhance the performance of the susceptibility model. The designed process thus enhances landslide prevention and preparedness on both local and regional scales, offering a crucial tool for mitigating the impact of landslides in South Korea.