Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm: A case study of Baihetan Reservoir Area

Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction. In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide suscep...

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Veröffentlicht in:Journal of mountain science Jg. 21; H. 3; S. 952 - 972
Hauptverfasser: Guo, Junqi, Xi, Wenfei, Yang, Zhiquan, Shi, Zhengtao, Huang, Guangcai, Yang, Zhengrong, Yang, Dongqing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Heidelberg Science Press 01.03.2024
Springer Nature B.V
Faculty of Public Safety and Emergency Management,Kunming University of Science and Technology,Kunming 650093,China
Key Laboratory of Geological Disaster Risk Prevention and Control and Emergency Disaster Reduction of Ministry of Emergency Management of the People's Republic of China,Kunming University of Science and Technology,Kunming 650093,China%Guizhou Geological Survey Institute,Guiyang 550081,China%College of Big Data and Intelligent Engineering,Southwest Forestry University,Kunming 650224,China
Key Laboratory of Early Rapid Identification,Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province,Kunming 650093,China%Key Laboratory of Early Rapid Identification,Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province,Kunming 650093,China
Faculty of Geography,Yunnan Normal University,Kunming 650500,China%Faculty of Geography,Yunnan Normal University,Kunming 650500,China
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ISSN:1672-6316, 1993-0321, 1008-2786
Online-Zugang:Volltext
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