Prediction of rainfall-induced landslide using machine learning models along highway Bandipora to Gurez road, India

The present study attempts to explore the efficacy of machine learning models in landslide predictions caused by rainfall events along highway from Bandipora to Gurez, J&K, India. Random forest (RF) and logistic regression (LR) models were employed to find the optimal parameters for targeted feature, i.e., landslide prediction. These models were evaluated for accuracy using the receiver operating characteristics, area under the curve (ROC-AUC) and false-negative rate (FNR). The results reveal a positive correlation between antecedent rainfall and landslide occurrence rather than between single-day landslide and rainfall events. Comparing the two models, LR model's performance is well within the acceptable limits of FNR and, therefore, could be preferred for landslide prediction over RF. LR model's incorrect prediction rate is 8.48% without including antecedent precipitation data and 5.84% including antecedent precipitation data. Our study calls for wider use of machinery learning models for developing early warning systems of landslides.