Precipitation-Triggered Landslide Prediction in Nepal Using Machine Learning and Deep Learning

Kelsey Doerksen, Yarin Gal, Freddie Kalaitzis, Cristian Rossi, David Petit, Sihan Li, Simon J. Dadson

Abstract: Landslides can be deadly natural disaster events, particularly in Nepal, where large earthquakes along the India-Asian collision zone and intense Monsoon rainfall can trigger widespread landslides. The complex nature of the landslide causal chain makes it difficult to predict these events, and existing derivations of the link between precipitation thresholds and landslides oversimplify the relationship, do not provide predictive abilities, and therefore limit their usefulness in disaster preparedness. This paper proposes to utilize the power of Machine Learning (ML) and Deep Learning (DL) Artificial Intelligence (AI) techniques with open-source, space-based data, to predict landslides at the District-level in Nepal at 7-, 10-, and 14-day temporal resolutions, using calibrated precipitation estimates and geomorphic data as input. Results provide both scientific insight via feature importance analysis, and a strong predictive capability of landslide prediction in Nepal using Random Forest and U-Net models.