Development of an advanced computational approach for early warning and predictions of rainfall-induced slope failures

Rainfall-induced landslides are among the most common natural hazards, threatening lives, homes, and infrastructure worldwide. This research develops a novel approach to predict landslides and provide early warning, even when field data are limited. The study creates advanced computer models that represent real soil conditions and simulate how slopes respond to heavy rainfall. These physics-based models are then combined with machine learning that learn continuously from monitoring data, allowing predictions to be updated and refined over time. By improving prediction accuracy, this research supports more effective landslide early warning systems and helps reduce damage and loss of life.