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An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, 430063, China.
Marine Intelligent Ship Engineering Research Center, Fujian Province Colleges and Universities, Minjiang University, Fuzhou, 350108, China.
Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing, 400060, China.
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, 430063, China.
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2019 (English)In: Energies, E-ISSN 1996-1073, Vol. 12, no 1, article id 112Article in journal (Refereed) Published
Abstract [en]

Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined by the cross-validation scheme. Both conventional and improved LSSVM models were applied in the short term forecasting of the water level in the middle reaches of the Yangtze River, China. Evaluations were made with both models through metrics such as RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percent Error) and index of agreement (d). More accurate forecasts were obtained although the improvement is regarded as moderate. Results indicate the capability and flexibility of LSSVM-type models in resolving time sequence problems. The improved LSSVM model is expected to provide useful water level information for the managements of hydroelectric resources in Rivers.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 12, no 1, article id 112
Keywords [en]
least squares support vector machine, water level forecasting, bias error control, Yangtze River
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-2377DOI: 10.3390/en12010112ISI: 000460665000112Scopus ID: 2-s2.0-85060052297Local ID: 28041OAI: oai:DiVA.org:mau-2377DiVA, id: diva2:1399130
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2024-06-17Bibliographically approved

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Malekian, Reza

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