A Global Empirical Model for Near‐Real‐Time Assessment of Seismically Induced Landslides

Abstract

Abstract Earthquake‐triggered landslides are a significant hazard in seismically active regions, but our ability to assess the hazard they pose in near‐real‐time is limited. In this study, we present a new globally applicable model for seismically induced landslides based on the most comprehensive global data set available; we use 23 landslide inventories that span a range of earthquake magnitudes and climatic and tectonic settings. We use logistic regression to relate the presence and distribution of earthquake‐triggered landslides with spatially distributed estimates of ground shaking, topographic slope, lithology, land cover type, and a topographic index designed to estimate variability in soil wetness to provide an empirical model of landslide distribution. We tested over 100 combinations of independent predictor variables to find the best fitting model, using a diverse set of statistical tests. Blind validation tests show that the model accurately estimates the distribution of available landslide inventories. The results indicate that the model is reliable and stable, with high balanced accuracy (correctly versus incorrectly classified pixels) for the majority of test events. A cross‐validation analysis shows high balanced accuracy for a majority of events as well. By combining near‐real‐time estimates of ground shaking with globally available landslide susceptibility data, this model provides a tool to estimate the distribution of coseismic landslide hazard within minutes of the occurrence of any earthquake worldwide for which a U.S. Geological Survey ShakeMap is available.