Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree.

Wang, Yi; Fang, Zhice; Hong, Haoyuan; Costache, Romulus; Tang, Xianzhe

Wang, Yi; Fang, Zhice; Hong, Haoyuan; Costache, Romulus; Tang, Xianzhe.

Affiliation

Wang Y; Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, 430205, China. Electronic address: cug.yi.wang@gmail.com.
Fang Z; Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China.
Hong H; Department of Geography and Regional Research, University of Vienna, Universitätsstraße 7, 1010, Vienna, Austria. Electronic address: a11915427@unet.univie.ac.at.
Costache R; Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, Brasov 500152, Romania; Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, Bucharest 050663, Romania; National Institute of Hydrology and Water Management, BucureÈti-PloieÈti Road
Tang X; Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan.

J Environ Manage ; 289: 112449, 2021 Jul 01.

Article de En | MEDLINE | ID: mdl-33812150

Sujet(s)
Mots clés

Bivariate statistics; Flood susceptibility mapping; Hybrid methods; Machine learning

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Catastrophes / Inondations Type d'étude: Prognostic_studies Pays/Région comme sujet: Asia Langue: En Journal: J Environ Manage Année: 2021 Type de document: Article Pays de publication: Royaume-Uni

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