Journal
ENERGIES
Volume 12, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/en12010112
Keywords
least squares support vector machine; water level forecasting; bias error control; Yangtze River
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Funding
- National Key Research and Development Program of China [2016YFC0402103]
- National Natural Science Foundation of China [51479155]
- Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University [SLK2018A02]
- Australia ARC DECRA [DE190100931]
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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.
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