Real-time forecasting of suspended sediment concentrations reservoirs by the optimal integration of multiple machine learning techniques

Real-time forecasting of suspended sediment concentrations reservoirs by the optimal integration of multiple machine learning techniques

Study region: Shihmen Reservoir is ranked the second largest designed storage capacity in Taiwan. Study focus: The accurate forecasting of suspended sediment concentrations (SSCs) during typhoons is critical for effective reservoir management. This paper proposes a two-step switched machine learning...

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Bibliographic Details
Main Authors: Cheng-Chia Huang, Ming-Jui Chang, Gwo-Fong Lin, Ming-Chang Wu, Po-Hsiang Wang
Format: Article
Language:English
Published: Elsevier 2021-04-01
Series:Journal of Hydrology: Regional Studies
Subjects:
Reservoir operation
Suspended sediment concentration
SRH-2D
Machine learning
Switched forecasting
Optimal integration
Online Access:http://www.sciencedirect.com/science/article/pii/S2214581821000331

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http://www.sciencedirect.com/science/article/pii/S2214581821000331

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