New Landslide Prediction Model Tracks Hidden Triggers Behind Deadly Disasters
A team of scientists from Northwestern University and UCLA has developed a powerful new landslide prediction framework that could transform how we prepare for and respond to deadly slope failures. Unlike traditional models that rely solely on rainfall, this innovative system tracks multiple water-related triggers including soil saturation and snowmelt offering sharper accuracy and broader coverage in landslide-prone regions.
Published in Geophysical Research Letters, the study introduces a data-driven, machine-learning approach that researchers say could revolutionize early warning systems and climate resilience planning in vulnerable areas like California.
Beyond Rainfall: Tracking the Water That Triggers Slides
"Current early warning systems often rely on past rainfall and histo...









