Machine learning-based landslide susceptibility mapping for short-term risk assessment in South Korea

Climate change impacts the frequency and intensity of extreme weather events, leading to an increase in natural disasters globally. Heavy rainfall is a notable extreme weather event, acting as an external factor for landslides. In South Korea, where approximately 70% of the terrain is mountainous, the susceptibility to landslides is high. Despite the development and implementation of landslide early warning systems by the Korea Forest Service for local governments, the extent of landslide damage has been significant, reaching approximately 2,345 hectares in the last five years. Especially, last year, landslides occurred more than 800 times with severe human costs. The current early warning system, which focuses on administrative boundaries, has limitations in accurately identifying high-vulnerability landslide areas. To address this issue, this study introduces a landslide diagnostic model designed to assess the daily susceptibility of South Korea with fine spatial resolution. The model employs a semi-automated process that encompasses the acquisition of short-term climate forecast data and the generation of daily landslide susceptibility maps. The core algorithm of the model is based on the random forest method, predicting susceptibility at a spatial resolution of 100 meters. The model integrates various feature datasets, including meteorological, topographic, and land surface data, which are closely linked to landslide occurrences. The training model utilized landslide inventory data from 2016 to 2022, with various performance indicators employed for calibration and validation. Additionally, the landslide inventory data from 2023 was utilized for final model verification. Notably, the model incorporates a 3-day climate forecast data process provided by the Korea Meteorological Administration, enabling the prediction of short-term daily landslide susceptibility. This landslide diagnostic model holds the potential to enhance landslide prevention and preparedness at both local and regional scales.