Prediction of urban water accumulation points and water accumulation process based on machine learning-Reference-Cited by-同舟云学术

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Prediction of urban water accumulation points and water accumulation process based on machine learning

Published:2021-09-18 Issue:4 Volume:14 Page:2317-2328
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Wang Hongfa,Zhao Yajuan,Zhou Yihong,Wang Huiliang

Funder

Key projects of Natural Science Foundation of China

Natural Science Foundation of China

Science and Technology Innovation Talents Project of Henan Education Department of China

Young backbone Teachers Training Fund of Henan Education Department of China

Excellent Youth Fund of Henan Province of China

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-021-00700-8.pdf

Reference64 articles.

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2. Bisht DS, Chatterjee C, Kalakoti S, Upadhyay P, Sahoo M, Panda A (2016) Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study. Nat Hazards 84(2):749–776. https://doi.org/10.1007/s11069-016-2455-1

3. Braud I, Breil P, Thollet F, Laagouy M, Branger F, Jacqueminet C, Kermadi S, Michel K (2013) Evidence of the impact of urbanization on the hydrological regime of a medium-sized periurban catchment in France. J Hydrol 485:5–23. https://doi.org/10.1016/j.jhydrol.2012.04.049

4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

5. Bubeck P, Aerts JCJH, de Moel H, Kreibich H (2016) Preface: flood-risk analysis and integrated management. Nat Hazard Earth Syst Sci 16(4):1005–1010. https://doi.org/10.5194/nhess-16-1005-2016

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