IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2018i1p112-d193953.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/1/112/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/1/112/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Xue & Lin, Cong & Wang, Yang & Zhao, Lingying & Duan, Na & Wu, Xudong, 2015. "Analysis of rural household energy consumption and renewable energy systems in Zhangziying town of Beijing," Ecological Modelling, Elsevier, vol. 318(C), pages 184-193.
    2. Zhang, Yan & Zhang, Jinyun & Yang, Zhifeng & Li, Shengsheng, 2011. "Regional differences in the factors that influence China’s energy-related carbon emissions, and potential mitigation strategies," Energy Policy, Elsevier, vol. 39(12), pages 7712-7718.
    3. Hatzigeorgiou, Emmanouil & Polatidis, Heracles & Haralambopoulos, Dias, 2011. "CO2 emissions, GDP and energy intensity: A multivariate cointegration and causality analysis for Greece, 1977-2007," Applied Energy, Elsevier, vol. 88(4), pages 1377-1385, April.
    4. Alajmi, Reema Gh, 2021. "Factors that impact greenhouse gas emissions in Saudi Arabia: Decomposition analysis using LMDI," Energy Policy, Elsevier, vol. 156(C).
    5. Lin, Boqiang & Ouyang, Xiaoling, 2014. "Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry," Energy, Elsevier, vol. 68(C), pages 688-697.
    6. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
    7. Huang, Jian-Bai & Luo, Yu-Mei & Feng, Chao, 2019. "An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030," Resources Policy, Elsevier, vol. 62(C), pages 541-549.
    8. Shahiduzzaman, Md. & Alam, Khorshed, 2013. "Changes in energy efficiency in Australia: A decomposition of aggregate energy intensity using logarithmic mean Divisia approach," Energy Policy, Elsevier, vol. 56(C), pages 341-351.
    9. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    10. Weidong Chen & Ruoyu Yang, 2018. "Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    11. Ang, B.W. & Liu, Na, 2007. "Negative-value problems of the logarithmic mean Divisia index decomposition approach," Energy Policy, Elsevier, vol. 35(1), pages 739-742, January.
    12. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.
    13. Lu, Qinli & Yang, Hong & Huang, Xianjin & Chuai, Xiaowei & Wu, Changyan, 2015. "Multi-sectoral decomposition in decoupling industrial growth from carbon emissions in the developed Jiangsu Province, China," Energy, Elsevier, vol. 82(C), pages 414-425.
    14. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    15. Ang, B.W. & Liu, Na, 2007. "Handling zero values in the logarithmic mean Divisia index decomposition approach," Energy Policy, Elsevier, vol. 35(1), pages 238-246, January.
    16. Oh, Ilyoung & Wehrmeyer, Walter & Mulugetta, Yacob, 2010. "Decomposition analysis and mitigation strategies of CO2 emissions from energy consumption in South Korea," Energy Policy, Elsevier, vol. 38(1), pages 364-377, January.
    17. Yeo, M.J. & Kim, Y.P., 2016. "Changes of the carbon dioxide emissions and the overshoot ratio resulting from the implementation of the 2nd Energy Master Plan in the Republic of Korea," Energy Policy, Elsevier, vol. 96(C), pages 241-250.
    18. Song, Ho-Jun & Lee, Seungmoon & Maken, Sanjeev & Ahn, Se-Woong & Park, Jin-Won & Min, Byoungryul & Koh, Wongun, 2007. "Environmental and economic assessment of the chemical absorption process in Korea using the LEAP model," Energy Policy, Elsevier, vol. 35(10), pages 5109-5116, October.
    19. Meral Buyukyildiz & Gulay Tezel & Volkan Yilmaz, 2014. "Estimation of the Change in Lake Water Level by Artificial Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4747-4763, October.
    20. Jalal Shiri & Shahaboddin Shamshirband & Ozgur Kisi & Sepideh Karimi & Seyyed M Bateni & Seyed Hossein Hosseini Nezhad & Arsalan Hashemi, 2016. "Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5217-5229, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:112-:d:193953. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.