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Open AccessArticle

Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir

1
Bureau of Hydrology, ChangJiang Water Resources Commission, Wuhan 430010, China
2
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
3
Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(1), 88; https://doi.org/10.3390/w11010088
Received: 26 November 2018 / Revised: 13 December 2018 / Accepted: 29 December 2018 / Published: 7 January 2019
(This article belongs to the Section Water Resources Management and Governance)
Operation rule plays an important role in the scientific management of hydropower reservoirs, because a scientifically sound operating rule can help operators make an approximately optimal decision with limited runoff prediction information. In past decades, various effective methods have been developed by researchers all the over world, but there are few publications evaluating the performances of different methods in deriving the hydropower reservoir operation rule. To achieve satisfying scheduling process triggered by limited streamflow data, four methods are used to derive the operation rule of hydropower reservoirs, including multiple linear regression (MLR), artificial neural network (ANN), extreme learning machine (ELM), and support vector machine (SVM). Then, the data from 1952 to 2015 in Hongjiadu reservoir of China are chosen as the survey case, and several quantitative statistical indexes are adopted to evaluate the performances of different models. The radial basis function is chosen as the kernel function of SVM, while the sigmoid function is used in the hidden layer of ELM and ANN. The simulations show that three artificial intelligence algorithms (ANN, SVM, and ELM) are able to provide better performances than the conventional MLR and scheduling graph method. Hence, for scholars in the hydropower operation field, the applications of artificial intelligence algorithms in deriving the operation rule of hydropower reservoir might be a challenge, but represents valuable research work for the future. View Full-Text
Keywords: hydropower reservoir; operation rule derivation; multiple linear regression; artificial neural network; extreme learning machine; support vector machine; dynamic programming hydropower reservoir; operation rule derivation; multiple linear regression; artificial neural network; extreme learning machine; support vector machine; dynamic programming
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Niu, W.-J.; Feng, Z.-K.; Feng, B.-F.; Min, Y.-W.; Cheng, C.-T.; Zhou, J.-Z. Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir. Water 2019, 11, 88.

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