Zhang, AomeiWang, XianminPedrycz, WitoldYang, QiyuanWang, XuewenGuo, Haixiang2025-04-182025-04-182024Zhang, A., Wang, X., Pedrycz, W., Yang, Q., Wang, X., & Guo, H. (2024). Near real-time spatial prediction of earthquake-triggered landslides based on global inventories from 2008 to 2022. Soil Dynamics and Earthquake Engineering, 185, 108890.02677261http://dx.doi.org/10.1016/j.soildyn.2024.108890https://hdl.handle.net/20.500.12713/6935Near 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. © 2024eninfo:eu-repo/semantics/closedAccessCoseismic LandslideFuzzy LearningGlobal ScaleNear Real-TimeSpatial PredictionArticle185WOS:0012920047000012-s2.0-85200785429Q110.1016/j.soildyn.2024.108890Q1