Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir-Reference-Cited by-同舟云学术

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Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir

Published:2021-02-13 Issue:9 Volume:35 Page:1805-1823
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Idrees Muhammad Bilal,Jehanzaib Muhammad,Kim Dongkyun,Kim Tae-Woong^ORCID

Funder

Ministry of the Interior and Safety

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-021-01982-6.pdf

Reference69 articles.

1. Anderson D, Moggridge H, Warren P, Shucksmith J (2015) The impacts of ‘run-of-river’ hydropower on the physical and ecological condition of rivers. Water Environ J 29:268–276. https://doi.org/10.1111/wej.12101

2. Annandale G, Morris G, Karki P (2016) Extending the life of reservoirs: sustainable sediment management for dams and run-of-river hydropower. World Bank, Washington

3. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000a) Artificial neural networks in hydrology. I: Preliminary Concepts. J Hydrol Eng 5:115–123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115)

4. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000b) Artificial neural networks in hydrology. II: Hydrologic applications. J Hydrol Eng 5:124–137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)

5. Aytek A, Kişi Ö (2008) A genetic programming approach to suspended sediment modelling. J Hydrol 351:288–298. https://doi.org/10.1016/j.jhydrol.2007.12.005

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