Predicting susceptibility to landslides under climate change impacts in metropolitan areas of South Korea using machine learning

Abstract

Landslides cause considerable damage to life and property worldwide. In order to prevent and respond to landslides, it is necessary to identify vulnerable areas. This study identified areas that are likely to be damaged by landslides and aimed to predict future landslides. We compared and analyzed areas using machine learning (ML) algorithms, and conducted susceptibility mapping and landslide prediction using an algorithm that produced excellent results. For landslide predictions, the probability distribution of precipitation in the representative concentration pathway scenario 8.5 was used. We accounted for future uncertainties by using several regional climate model scenarios. Comparing the performances of different ML algorithms, the overall prediction accuracy of the random forest (0.932) was excellent. Susceptibility to landslides in the future determined using the random forest and five other regional climate models exhibited minor differences, but the average susceptibility increased over time. In addition, many urban areas are distributed around forest areas that have high landslide vulnerabilities, which provide important perspectives for urban and environmental planning.