Flood forecasting using a hybrid extreme learning machine-particle swarm optimization algorithm (ELM-PSO) model-Reference-Cited by-同舟云学术

Flood forecasting using a hybrid extreme learning machine-particle swarm optimization algorithm (ELM-PSO) model

Published:2019-11-07 Issue:1 Volume:6 Page:341-347
ISSN:2363-6203
Container-title:Modeling Earth Systems and Environment
language:en
Short-container-title:Model. Earth Syst. Environ.

Author:

Anupam Sagnik^ORCID,Pani Padmini^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computers in Earth Sciences,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,General Environmental Science

Link

http://link.springer.com/content/pdf/10.1007/s40808-019-00682-z.pdf

Reference22 articles.

1. Amarnath G, Alahacoon N, Gismalla Y, Mohammed Y, Sharma BR, Smakhtin V (2016) Increasing early warning lead time through improved transboundary flood forecasting in the Gash river basin, Horn of Africa. In: Flood forecasting. Elsevier, pp 183–200. https://doi.org/10.1016/B978-0-12-801884-2.00008-6

2. Bhattacharya B, Solomatine DP (2005) Neural networks and M5 model trees in modelling water level-discharge relationship. Neurocomputing 63:381–396. https://doi.org/10.1016/j.neucom.2004.04.016

3. Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35(4):1191–1197. https://doi.org/10.1029/1998WR900086

4. Central Water Commission (2015) Policy and Advisory Technical Assistance 8089 IND Phase II—operational research to support mainstreaming of integrated flood management under climate change. (Vol. 2., Basin Flood Management Plan Brahmani-Baitarani, Odisha). http://mowr.gov.in/sites/default/files/NWM_OR-FM-CC_2015_Vol-2_0.pdf

5. Central Water Commission (2019) Standard operating procedure for flood forecasting, April 2019. http://cwc.gov.in/sites/default/files/admin/SOP_ff_apr_19.pdf

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