An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level
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 determine...
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doaj-e3bd14b88ec84dee9a47588d596768492020-11-25T01:50:49ZengMDPI AGEnergies1996-10732018-12-0112111210.3390/en12010112en12010112An Improved LSSVM Model for Intelligent Prediction of the Daily Water LevelTao Guo0Wei He1Zhonglian Jiang2Xiumin Chu3Reza Malekian4Zhixiong Li5National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, ChinaMarine Intelligent Ship Engineering Research Center of Fujian Province Colleges and Universities, Minjiang University, Fuzhou 350108, ChinaKey Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing 400060, ChinaNational Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, ChinaDepartment of Computer Science and Media Technology, Malmö University, 20506 Malmö, SwedenSchool of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaDaily 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.http://www.mdpi.com/1996-1073/12/1/112least squares support vector machinewater level forecastingbias error controlYangtze River |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tao Guo Wei He Zhonglian Jiang Xiumin Chu Reza Malekian Zhixiong Li |
spellingShingle |
Tao Guo Wei He Zhonglian Jiang Xiumin Chu Reza Malekian Zhixiong Li An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level Energies least squares support vector machine water level forecasting bias error control Yangtze River |
author_facet |
Tao Guo Wei He Zhonglian Jiang Xiumin Chu Reza Malekian Zhixiong Li |
author_sort |
Tao Guo |
title |
An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level |
title_short |
An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level |
title_full |
An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level |
title_fullStr |
An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level |
title_full_unstemmed |
An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level |
title_sort |
improved lssvm model for intelligent prediction of the daily water level |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2018-12-01 |
description |
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. |
topic |
least squares support vector machine water level forecasting bias error control Yangtze River |
url |
http://www.mdpi.com/1996-1073/12/1/112 |
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