Rapid prediction of the magnitude scale of landslide events triggered by an earthquake

The severity of earthquake-induced landslide-events can be quantified by the landslide-event magnitude, a metric derived from the frequency-size distribution of landslide inventories. However, inventories for all earthquakes do not exist, because the preparation of a suitable inventory requires data, time and expertise. Prediction of landslide-event magnitude immediately following an earthquake provides an estimate of total landslide area and volume based on empirical relations, allows the assessment of the severity of a landslide-event in near real-time and to estimate the frequency-size distribution curve of the landslides. In this study, we use 23 earthquake-induced landslide inventories and propose a method to predict landslide-event magnitude. We select five predictors, both morphometric and seismogenic, which are globally and readily available. We use the predictors within a stepwise linear regression, validated using the leave-one-out technique. We show that our approach successfully predicts landslide-event magnitude values and provides results along with their statistical significance and confidence levels. However, to test the validity of the approach globally, it should be calibrated using a larger and more representative dataset. A global, near real-time assessments regarding landslide-event magnitude scale can then be achieved by retrieving the readily available ShakeMaps, along with topographic and thematic information, and applying the calibrated model. The results may provide valuable information regarding landscape evolution processes, landslide hazard assessments and contribute to the rapid emergency response after earthquakes in mountainous terrain.