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

• Main Authors: Tao Guo, Wei He, Zhonglian Jiang, Xiumin Chu, Reza Malekian, Zhixiong Li
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Energies
• Subjects: least squares support vector machine; water level forecasting; bias error control; Yangtze River
• Online Access: http://www.mdpi.com/1996-1073/12/1/112

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.