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

Author

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  • 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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    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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    3. Xinxin He & Jungang Luo & Peng Li & Ganggang Zuo & Jiancang Xie, 2020. "A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 865-884, January.
    4. Mojtaba Poursaeid & Amir Houssain Poursaeid & Saeid Shabanlou, 2022. "A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1499-1519, March.
    5. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
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    Cited by:

    1. Mojtaba Poursaeid & Amir Hossein Poursaeed & Saeid Shabanlou, 2025. "Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1073-1087, February.
    2. Saleh Alsulamy & Vijendra Kumar & Ozgur Kisi & Naresh Kedam & Namal Rathnayake, 2025. "Enhancing Water Level Prediction Using Ensemble Machine Learning Models: A Comparative Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(8), pages 3995-4014, June.
    3. Anas Rahimi & Noor Kh. Yashooa & Ali Najah Ahmed & Mohsen Sherif & Ahmed El-shafie, 2025. "Different Time-Increment Rainfall Prediction Models: a Machine Learning Approach Using Various Input Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(4), pages 1677-1696, March.

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