An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level

Tao Guo, Wei He, Zhonglian Jiang, Xiumin Chu, Reza Malekian and Zhixiong Li
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Tao Guo: National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Wei He: Marine Intelligent Ship Engineering Research Center of Fujian Province Colleges and Universities, Minjiang University, Fuzhou 350108, China
Zhonglian Jiang: Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing 400060, China
Xiumin Chu: National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Reza Malekian: Department of Computer Science and Media Technology, Malmö University, 20506 Malmö, Sweden
Zhixiong Li: School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia

Energies, 2018, vol. 12, issue 1, 1-11

Abstract: 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.

Keywords: least squares support vector machine; water level forecasting; bias error control; Yangtze River (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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