Scientists develop dynamic landslide prediction method using hydrological and machine learning data

Scientists develop dynamic landslide prediction method using hydrological and machine learning data

Northwestern University and University of California, Los Angeles (UCLA) scientists have developed a new process-based framework that provides a more accurate and dynamic approach to landslide prediction over large areas.

Identifying landslide threats using hydrological predictors

Current methods to predict landslides rely primarily on rainfall intensity. New model combines various water-related factors with machine learning. When applied to more than 600 landslides in California, model identified the conditions that caused 89% of the events.