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

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Listed:
  • 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)

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.

Suggested Citation

  • Tao Guo & Wei He & Zhonglian Jiang & Xiumin Chu & Reza Malekian & Zhixiong Li, 2018. "An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level," Energies, MDPI, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:112-:d:193953
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    References listed on IDEAS

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    1. Choi, Ki-Hong & Ang, B. W., 2001. "A time-series analysis of energy-related carbon emissions in Korea," Energy Policy, Elsevier, vol. 29(13), pages 1155-1161, November.
    2. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
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    Cited by:

    1. Muhammad Ali Musarat & Wesam Salah Alaloul & Muhammad Babar Ali Rabbani & Mujahid Ali & Muhammad Altaf & Roman Fediuk & Nikolai Vatin & Sergey Klyuev & Hamna Bukhari & Alishba Sadiq & Waqas Rafiq & Wa, 2021. "Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    2. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.

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