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

Min Gan, Xijun Lai (), Yan Guo, Yongping Chen, Shunqi Pan and Yinghao Zhang
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Min Gan: Chinese Academy of Sciences
Xijun Lai: Chinese Academy of Sciences
Yan Guo: Hohai University
Yongping Chen: Hohai University
Shunqi Pan: Cardiff University
Yinghao Zhang: Chinese Academy of Sciences

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 13, No 18, 5305-5321

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.

Keywords: Ensemble learning; Floodplain; LightGBM; Poyang Lake; Water level prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03915-8

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