A Pilot Machine-Learning Framework for Cumulative Antecedent Rainfall Threshold in Landslide Assessment Along Jalan Simpang Pulai–Blue Valley, Malaysia

Abstract

Rainfall-induced landslides along Malaysian highway cut slopes are commonly evaluated using empirical thresholds, with limited consideration of cumulative antecedent rainfall. This study evaluates a machine-learning rainfall threshold for five documented shallow-slope failures along Federal Route FT185 (Jalan Simpang Pulai–Blue Valley), Perak, Malaysia, using 520 site-days of daily rainfall records. Cumulative rainfall was calculated over seven windows (1, 3, 5, 7, 14, 21 and 30 days), together with a short-burst intensity feature and a wet-day count and six classifiers (logistic regression, Random Forest, Gradient Boosting, k-nearest neighbours, a support vector machine and XGBoost) were trained under class-weighted, leave-one-site-out cross-validation. Random Forest achieved the best discrimination (ROC-AUC = 0.817), with 80% sensitivity and 90% specificity. Antecedent rainfall consistently ranked above same-day rainfall, indicating the importance of longer-duration rainfall accumulation in the present dataset. An interpretable two-feature logistic regression boundary (14-day antecedent rainfall vs. same-day rainfall) and Random Forest partial-dependence saturation points (approximately 203, 330 and 447 mm for the 14-, 21- and 30-day windows) were derived and combined into a two-tier watch/warning rainfall-probability threshold. The results provide preliminary, site-specific rainfall thresholds for the investigated corridor within a pilot methodological framework requiring prospective validation with additional independent failure events before operational application.