FRAMEWORK FOR DETERMINING PRIORITIZATION OF COUNTERMEASURES AGAINST RAINFALL-INDUCED SLOPE FAILURE USING MACHINE LEARNING AND PROBABILISTIC RAINFALL INTENSITY

Abstract

Rainfall-induced slope failures can cause road closures and a significant delay in the recovery process. Given the increase in the frequency of extreme rainfall events, the prioritization of countermeasures against slope failures is needed to achieve the disaster mitigation. In this paper, the methodology for determining the prioritization of countermeasures against slope failures due to heavy rainfall using machine learning and probabilistic rainfall intensity is presented. The rainfall index calculated based on long- and short-term effective rainfalls is used as one of the explanatory variables representing a rainfall intensity measure considering the effects of time variation of rainfall on the occurrence of slope failure. The training data used in the machine learning are re-sampled to avoid overfitting caused by imbalanced class samples. Random Forest and LightGBM are used to develop the prediction model of rainfall-induced slope failure. As a result, LightGBM shows better performance than Random Forest. In addition, the probabilistic rainfall intensity is defined as the rainfall index corresponding to a return period and estimated using the generalized extreme value distribution. The locations where slope failures can occur are evaluated using the prediction model based on LightGBM and the probabilistic rainfall intensity. A illustrative example demonstrates that the proposed methodology can be used to determine the countermeasure prioritization based on return period.