A Data-Driven and Hybrid Approach for Real-Time Threshold-Based Landslide Prediction in Hilly Regions using Sensor Networks

Abstract

ABSTRACT Landslides pose a constant threat to the Himalayan regions, causing severe harm to people and property, and disastrous consequences demand novel ways of early prediction and prevention. The study gives a comprehensive insight into real-time landslide prediction using data from several sensors. The study investigates the applicability of advanced machine learning algorithms such as Multiple Linear Regression (MLR), XGBoost (XGB), Random Forest (RF), and a hybrid MLR-LSTM model in predicting landslide disasters. Further, the performance of these models was evaluated by utilizing the evaluation metrics such as MSE, MAE, and RMSE. However, the hybrid MLR-LSTM model excels with MSE = 0.014, MAE = 0.140, and RMSE = 0.120. Additionally, the study demonstrates the hybrid model’s practical application by demonstrating the real-time alert generation method.