Near real-time spatial prediction of earthquake-triggered landslides based on global inventories from 2008 to 2022

Abstract

Near real-time prediction of earthquake-triggered landslides can rapidly forecast the spatial distribution of coseismic landslides just after a great earthquake, and provide effective support for emergency response. However, the prediction of earthquake-triggered landslides has always been a great challenge because of low accuracy and high false alarms. This work proposes a novel fuzzy deep learning (FuDL) model for near real-time earthquake-triggered landslide spatial prediction. Fuzzy learning theory is for the first time employed in earthquake-triggered landslide prediction. The FuDL has high generalization and robustness, effectively improving the accuracy of earthquake-triggered landslide prediction. Eighteen earthquake-triggered landslide inventories worldwide from 2008 to 2022 are employed to conduct ETL prediction. According to the chronological order, 15 earthquake-triggered landslides from 2008 to 2018 are adopted to train the FuDL model, and 3 earthquake-triggered landslides from 2019 to 2022 are utilized for near real-time earthquake-triggered landslide prediction. Furthermore, this work reveals that ground movement, relatively steep and high topography, and strong seismic intensity are critical factors affecting the spatial distribution of earthquake-triggered landslides. In addition, this work conducted a detailed analysis of the distribution patterns of earthquake-triggered landslides on a global scale.