Machine Learning Prediction of Co-Seismic Landslide with Distance and Azimuth Instead of Peak Ground Acceleration

Abstract

Most machine learning (ML) studies on predicting co-seismic landslides have relied on Peak Ground Acceleration (PGA). The PGA of the ground strongly correlates with the relative position and azimuth of the seismogenic faults. Using the co-seismic landslide records of the 2008 Wenchuan earthquake, we show that the ML model using the distances and azimuths from the epicenter to sites performs better than the PGA model regarding accuracy and actual prediction results. The distances and azimuths are more accessible than the PGA because obtaining accurate and realistic large-scale PGAs is difficult. Considering their computational efficiency and cost-effectiveness, the ML models utilizing distances and azimuths from the epicenter to the sites as inputs could be an alternative to PGA-based models for evaluating the impact of co-seismic landslides. Notably, these models prove advantageous in near-real-time scenarios and settings requiring high spatial resolution, and provide favorable assistance in achieving the goal of sustainable development of society.