Prediction of precipitation-induced landslides and sediment discharge at the basin scale using machine learning

Abstract. During heavy rainfall, sediment discharge from mountainous regions is exacerbating damage in downstream urban areas. Therefore, predicting sediment discharge from mountainous area is of critical importance. A large proportion of discharged sediment is produced by slope failures and subsequently transported through channel networks. Although topographic and geotechnical conditions vary within a watershed, both the susceptibility to slope failure and the volume of sediment produced depend strongly on these conditions. In this study, we develop a machine learning model that predicts slope failure occurrence and landslide volume from topographic and geotechnical parameters. By coupling this model with a rainfall and sediment runoff, we propose an integrated framework that simulates the entire process from slope failure to sediment production and downstream transport at the watershed scale. The proposed model incorporates uncertainties associated with unaccounted variability through a probabilistic representation, enabling the evaluation of multiple plausible scenarios. The model was applied to the Pekerebetsu basin for the 2016 Hokkaido heavy rainfall event. Repeated simulations under identical topographic, geotechnical, and rainfall conditions produced slightly different spatial patterns and numbers of slope failures. However, all simulations reproduced sediment production and discharge close to observed values. These results demonstrate that the proposed framework can consistently capture watershed-scale sediment dynamics while accounting for inherent variability in slope failure processes.