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Advanced water level prediction for a large-scale river–lake system using hybrid soft computing approach: a case study in Dongting Lake, China

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Abstract

Water level prediction is vital in developing a sustainable conceptual design of water infrastructures, providing flood and drought control measures, etc. However, due to the complexity and many other inter-related influencing factors within a catchment, water level prediction remains a challenging task. A reliable method that is able to extract the non-linear behaviors of various parameters effectively, and thus enhances the modelling capability in terms of computation time and accuracy is required. Therefore, the Dongting Lake of China, a large-scale river–lake system has been selected for this study. The main aim is to provide a practical method for advanced water level prediction at the downstream outlet of Dongting Lake for flood warning purposes. The novelty of this study is the adoption of a soft computing modelling approach, based on minimum input requirements to reduce its dependency on too many inputs which might limit its functionality in the future. The results obtained show that the model developed can predict the hourly water level in Dongting Lake accurately with an error of 1.2%. It is able to provide an advanced water level prediction of 21 h ahead of the time step, and thus applicable for early flood warning to the surrounding area with densely populated townships.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (#51979015, #51839002), Partial support comes from the National Science Foundation of Hunan Province, China (#2018JJ3535), Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province (#2020DT002), and Universiti Tunku Abdul Rahman Research Fund (IPSR/RMC/UTARRF/2020-C2/C04).

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Authors and Affiliations

  1. School of Hydraulic Engineering, Changsha University of Science and Technology, Changsha, 4100114, China

    Bin Deng & Changbo Jiang

  2. Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province, Changsha, 410114, China

    Bin Deng & Changbo Jiang

  3. Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, 410114, China

    Bin Deng & Changbo Jiang

  4. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Sai Hin Lai, Pavitra Kumar & Ahmed El-Shafie

  5. Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000, Kajang, Selangor, Malaysia

    Ren Jie Chin

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  1. Bin Deng
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Deng, B., Lai, S.H., Jiang, C. et al. Advanced water level prediction for a large-scale river–lake system using hybrid soft computing approach: a case study in Dongting Lake, China. Earth Sci Inform 14, 1987–2001 (2021). https://doi.org/10.1007/s12145-021-00665-8

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