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Weekly streamflow forecasting of Rhine river based on machine learning approaches

Author

Listed:
  • Zohreh Sheikh Khozani

    (Paleoclimate Dynamics Group, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research)

  • Elimar Precht

    (Paleoclimate Dynamics Group, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research)

  • Monica Ionita

    (Paleoclimate Dynamics Group, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research
    “Stefan cel Mare” University of Suceava)

Abstract

The Rhine River is a vital waterway in Europe, crucial for navigation, hydropower generation, and ecosystem health. Thus, accurately forecasting its streamflow is essential for effective water resource management. This study explored the utilization of several Machine Learning (ML) techniques including Multi-layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost), for forecasting weekly streamflow for the Rhine River. Meteorological data (e.g., precipitation, temperature, vapor pressure deficit) collected from meteorological stations situated on the main river course (i.e., Mannheim and Worms) spanning from 2013 to 2023 were used as predictors. Two scenarios were considered for predicting weekly streamflow according to the results of the best input combination. According to the results streamflow is most significantly predicted by precipitation, vapor pressure, and relative humidity, while average and maximum temperatures play a smaller role. Various quantitative and visually-oriented evaluation metrics were employed to validate and compare the performance of the proposed models. It found that the XGBoost outperformed than other algorithms in prediction of weekly streamflow in Rhine River. Overall, weekly streamflow forecasting for the Rhine River is crucial for effective water resource management, navigation, hydropower generation, flood control, and ecosystem health. By providing timely insights into flow variations and identifying the optimal predictors, weekly forecasts empower stakeholders to make informed decisions and ensure the Rhine’s continued sustainability.

Suggested Citation

  • Zohreh Sheikh Khozani & Elimar Precht & Monica Ionita, 2025. "Weekly streamflow forecasting of Rhine river based on machine learning approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(4), pages 4135-4153, March.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06962-x
    DOI: 10.1007/s11069-024-06962-x
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    References listed on IDEAS

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    1. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
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