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

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorZhang, Aomei
dc.contributor.authorWang, Xianmin
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYang, Qiyuan
dc.contributor.authorWang, Xuewen
dc.contributor.authorGuo, Haixiang
dc.date.accessioned2025-04-18T10:04:21Z
dc.date.available2025-04-18T10:04:21Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractNear 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. © 2024
dc.description.sponsorshipThis work is funded by the National Natural Science Foundation of China (U21A2013, 42311530065, 71874165), Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (Grant Nos. GLAB2020ZR02, GLAB2022ZR02), State Key Laboratory of Biogeology and Environmental Geology (Grant No. GBL12107), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUG2642022006) and the Foundation for Innovative Research Groups of Hubei Province of China (Grant No.2024AFA015).
dc.identifier.citationZhang, 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.
dc.identifier.doi10.1016/j.soildyn.2024.108890
dc.identifier.issn02677261
dc.identifier.scopus2-s2.0-85200785429
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.soildyn.2024.108890
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6935
dc.identifier.volume185
dc.identifier.wosWOS:001292004700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofSoil Dynamics and Earthquake Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCoseismic Landslide
dc.subjectFuzzy Learning
dc.subjectGlobal Scale
dc.subjectNear Real-Time
dc.subjectSpatial Prediction
dc.titleNear real-time spatial prediction of earthquake-triggered landslides based on global inventories from 2008 to 2022
dc.typeArticle

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