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.
Highlights
Time series forecasting has been recognized as one of the classical problems in the fields of both energy engineering and science [1], among which daily water level forecasting is closely related to the hydroelectric resource utilization [2]
The daily water level forecasting is of significant importance for the maritime administration and water transport safety
An improved LSSVMi model was proposed through a bias error control scheme
Summary
Time series forecasting has been recognized as one of the classical problems in the fields of both energy engineering and science [1], among which daily water level forecasting is closely related to the hydroelectric resource utilization [2]. In view of the complex intrinsic mechanism and multiple influencing factors, artificial intelligence methods, e.g., adaptive network based fuzzy inference system [1,2] and neural network [3,4] have been accepted and extensively applied to resolve time series forecasting problems. The authors conclude that SVM and ANN have an edge over the results by the conventional RC (Rating Curve) and MLR (Multiple Linear Regression) models. This is more obvious for peak value predictions. The present study focuses on the short-term forecasting of the daily water level by using an improved LSSVM model.
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