Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis-Reference-Cited by-同舟云学术

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Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis

Published:2019-10-14 Issue:3 Volume:3 Page:585-601
ISSN:2509-9426
Container-title:Earth Systems and Environment
language:en
Short-container-title:Earth Syst Environ

Author:

Rahman Mahfuzur,Ningsheng Chen,Islam Md Monirul,Dewan Ashraf,Iqbal Javed,Washakh Rana Muhammad Ali,Shufeng Tian

Funder

National Natural Science Foundation of China

135 Strategic Program of the Institute of Mountain Hazards and Environment (IMHE), Chinese Academy of Sciences

Publisher

Springer Science and Business Media LLC

Subject

Computers in Earth Sciences,Economic Geology,Geology,Environmental Science (miscellaneous),Global and Planetary Change

Link

http://link.springer.com/content/pdf/10.1007/s41748-019-00123-y.pdf

Reference83 articles.

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2. Arabameri A, Pradhan B, Rezaei K, Yamani M, Pourghasemi HR, Lombardo L (2018) Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm. Land Degrad Dev 29:4035–4049

3. Arabameri A, Pradhan B, Rezaei K, Sohrabi M, Kalantari Z (2019) GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci 16:595–618

4. Arora M, Das Gupta A, Gupta R (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25:559–572

5. Asare-Kyei D, Forkuor G, Venus V (2015) Modeling flood hazard zones at the sub-district level with the rational model integrated with GIS and remote sensing approaches. Water 7:3531–3564

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