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A New Whole Life Cycle Index System for Evaluation of Runoff Forecasting

Author

Listed:
  • Xiaohui Yuan

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Wenbin Hu

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Chao Wang

    (China Institute of Water Resources and Hydropower Research)

  • Yuxin Mao

    (China Yangtze Power Co., Ltd.)

Abstract

Runoff forecasting is crucial for planning and managing water resources. As hydrological data becomes more available, more data-driven models are being employed to enhance the effectiveness of runoff forecasting. To accurately and quantitatively assess the whole runoff forecast process, from model construction to application, a whole life cycle forecasting evaluation index system was established in this study to evaluate the data, factors, sample integrity, model construction, and forecast result. The analysis included quantitative evaluation criteria to comprehensively consider data quality, forecasting factor characterization, sample representativeness, model generalization, and result quality. An evaluation of forecast results from 7 river basins and their 85 hydrological stations in China showed that the proposed index system could accurately reflect the performance of the forecasting process. The overall performance of the forecasting model and process can be evaluated quantitatively based on the Euclidean distance, and the pathways to improve the forecasting effectiveness of the model can be identified based on the evaluation results. The validity of the proposed index is also experimentally demonstrated. The proposed index system can be applied to the evaluation of data-driven forecasting processes in various fields.

Suggested Citation

  • Xiaohui Yuan & Wenbin Hu & Chao Wang & Yuxin Mao, 2024. "A New Whole Life Cycle Index System for Evaluation of Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(4), pages 1419-1435, March.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:4:d:10.1007_s11269-023-03728-1
    DOI: 10.1007/s11269-023-03728-1
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    References listed on IDEAS

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    1. Y. R. Fan & G. H. Huang & Y. P. Li & X. Q. Wang & Z. Li, 2016. "Probabilistic Prediction for Monthly Streamflow through Coupling Stepwise Cluster Analysis and Quantile Regression Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5313-5331, November.
    2. K. S. Kasiviswanathan & Jianxun He & Joo-Hwa Tay & K. P. Sudheer, 2019. "Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 229-243, January.
    3. Yani Lian & Jungang Luo & Jingmin Wang & Ganggang Zuo & Na Wei, 2022. "Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 21-37, January.
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