Spatial Prediction of the Future Landslide Susceptibility under the SSP Scenario Using Machine learning Algorithms

The increase in localized heavy rainfall and intense storms due to climate change has led to a continuous rise in landslide damages in South Korea, including slope failures and debris flows. While post-landslide recovery and damage site assessments are crucial, it is equally important to develop proactive and systematic landslide adaptation strategies to predict and prepare for landslides in advance. This study aims to develop an interpretable machine learning-based landslide susceptibility model and analyze landslide-prone areas under future climate change scenarios. Through this approach, it seeks to clearly identify the impact of forest management factors on landslides and establish effective adaptation strategies tailored to climate change scenarios. A dataset comprising 6,517 recorded landslide events from 2011 to 2024 was utilized. Various external and internal conditioning factors were obtained and constructed with a resolution of 100 meters. Climate scenario analysis employed SSP 1-2.6, SSP 2-4.5, and SSP 5-8.5, with extreme climate factors including rainfall intensity, the number of heavy rain days, daily rainfall, and 5day cumulative rainfall. Notably, changes in stand age class, DBH class, and growing stock under future forest management scenarios were calculated and integrated into the landslide model, enabling an evaluation of how management strategies affect landslide susceptibility. Results were validated by comparing past actual occurrence data. The SSP 5-8.5 scenario indicates a significant increase in landslide occurrences. These findings provide valuable insights into the effects of climate change on landslide susceptibility in South Korea and examine the potential of future landslide management strategies to mitigate associated susceptibility.