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Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM

Author

Listed:
  • Min Gan

    (Chinese Academy of Sciences
    Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences)

  • Xijun Lai

    (Chinese Academy of Sciences
    Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences)

  • Yan Guo

    (Hohai University)

  • Yongping Chen

    (Hohai University)

  • Shunqi Pan

    (Cardiff University)

  • Yinghao Zhang

    (Chinese Academy of Sciences)

Abstract

Highlights A LightGBM-based water level prediction model was developed for floodplain lakes. The RMSE values of the model’s one-day-ahead prediction range from 0.09 to 0.10 m. The rank of the driving factors of Poyang Lake water level change was identified.

Suggested Citation

  • Min Gan & Xijun Lai & Yan Guo & Yongping Chen & Shunqi Pan & Yinghao Zhang, 2024. "Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5305-5321, October.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03915-8
    DOI: 10.1007/s11269-024-03915-8
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    References listed on IDEAS

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    1. 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.
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