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A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction

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  • Ming Zhong

    (Sun Yat-Sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
    Sun Yat-Sen University)

  • Hongrui Zhang

    (Sun Yat-Sen University)

  • Tao Jiang

    (Sun Yat-Sen University)

  • Jun Guo

    (Huazhong University of Science and Technology)

  • Jinxin Zhu

    (Sun Yat-Sen University)

  • Dagang Wang

    (Sun Yat-Sen University)

  • Xiaohong Chen

    (Sun Yat-sen University)

Abstract

Climate warming will accelerate the global hydrological cycle and intensify the risk of extreme precipitation and floods. Accurate and reliable streamflow forecasting is fundamental to flood risk mitigation. In this study, we develop a streamflow prediction model by coupling physics-based models, namely, the variable infiltration capacity (VIC) and catchment-based macroscale floodplain (CaMa-Flood) models, with deep learning methods, i.e., the recurrent neural network (RNN) and long short-term memory (LSTM), which complement physics-based models. Two hybrid models, namely, the VIC-CaMa-Flood-RNN (VCR) and VIC-CaMa-Flood-LSTM (VCL) models, are established that provide the advantages of both physics-based and data-driven models. The results show that (1) the VCL model achieves the best performance among the proposed models in streamflow and flood prediction. It outperforms the VCR model, with a potential increase of up to 4.94% in Nash Sutcliffe efficiency coefficient (NSE) and 1.18% in correlation coefficient (R), as well as an improvement of 15.8% in the maximum flood volumes (MAX). (2) in this study, we investigate the actual contribution of various input features (precipitation, maximum temperature, minimum temperature, and wind speed) to the hybrid model-simulated streamflow. The results show that the minimum temperature is the most significant feature, followed by precipitation, maximum temperature, and wind speed. When the maximum and minimum temperatures are considered as temperature features, temperature and precipitation are the most important features affecting the hybrid model-simulated streamflow, with the actual contribution exceeding 80%. (3) during the 2040 and 2090 s, considering the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the monthly average streamflow will increase with increasing temperature, and flood seasons will be prolonged. This study is a novel attempt to couple physics-based and data-driven models, which can further improve the streamflow and flood prediction accuracy and provide reliable support for future flood risk assessments.

Suggested Citation

  • Ming Zhong & Hongrui Zhang & Tao Jiang & Jun Guo & Jinxin Zhu & Dagang Wang & Xiaohong Chen, 2023. "A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4841-4859, September.
  • Handle: RePEc:spr:waterr:v:37:y:2023:i:12:d:10.1007_s11269-023-03583-0
    DOI: 10.1007/s11269-023-03583-0
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    References listed on IDEAS

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    1. Adisa Hammed Akinsoji & Bashir Adelodun & Qudus Adeyi & Rahmon Abiodun Salau & Golden Odey & Kyung Sook Choi, 2024. "Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4735-4761, September.
    2. Bisrat Ayalew Yifru & Kyoung Jae Lim & Seoro Lee, 2024. "Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review," Sustainability, MDPI, vol. 16(4), pages 1-27, February.

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