Predicting Co-seismic Landslides Using Distance and Azimuth Achieves Better Results

Abstract Most machine learning (ML) studies in predicting co-seismic landslides relied on the Peak Ground Acceleration (PGA). Yet, the accuracy of PGA-based models can be compromised due to the limited availability of near-source observations and spatial information regarding seismic faults. This limitation can result in significant discrepancies between the accumulated PGA and the actual ground shaking, which cannot be overlooked. Here we show the ML model using the distance and azimuth to the epicenter performs better than the model using PGA in terms of accuracy as well as actual prediction results. Considering their computational efficiency and cost-effectiveness, the ML models utilizing distance and azimuth to the epicenter as inputs could be used as an alternative to PGA-based models for evaluating the impact of co-seismic landslides. Particularly, these models prove advantageous in near real-time scenarios and settings that require high spatial resolution.