Rapid Estimation of Earthquake Induced Landslides using Machine Learning Models: Insights from Haiti Earthquakes

Abstract

Strong earthquakes on mountain slopes can trigger numerous landslides, a significant secondary hazard responsible for a substantial proportion of fatalities in the affected area. In this study, we present a model framework for rapidly creating coseismic landslide probability distribution maps using machine learning models and optimal conditioning factors. To illustrate our approach, we focus on the case of the Mw 7.2 Haiti earthquake in 2021 and predict the distribution of coseismic landslides based on historical landslide data collected following the Mw 7.0 Haiti earthquake in 2010. To validate our findings, we mapped all the landslides triggered during the 2021 event. Furthermore, we conduct a comparative analysis of various landslide-conditioning factors (seismic, topographic, lithologic, and hydrological variables) in relation to the coseismic landslides occurring during both earthquake events in 2010 and 2021, to reassess the factors feed into the machine learning model. We observed noticeable differences in patterns of several conditioning factors between the two events EQIL distributions (e.g., tectonic and releif factors), but consistent similarities in other terrain factors (e.g., slope, curvature, topographic wetness index, etc.). Our Random Forest (RF) model, initially trained using the 2010 landslide inventory and 15 selected factors, effectively predicts 2021 landslides with an area under curve (AUC) score of 0.83. Improved performance is achieved when we use a reevaluated set of six factors for training, resulting in an AUC score of 0.90, with  93% of landslides falling into the high to medium probability class. These findings demonstrate the feasibility of rapidly generating highly accurate coseismic landslide distribution maps, even when there are considerable differences in key conditioning factors, highlighting the applicability of ML models to complex problems.