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Middle and Long-Term Runoff Probabilistic Forecasting Based on Gaussian Mixture Regression

Author

Listed:
  • Yongqi Liu

    (Huazhong University of Science and Technology)

  • Lei Ye

    (Dalian University of Technology)

  • Hui Qin

    (Huazhong University of Science and Technology)

  • Shuo Ouyang

    (Changjiang Water Resources Commission)

  • Zhendong Zhang

    (Huazhong University of Science and Technology)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology)

Abstract

Reliable forecasts of middle and long-term runoff can be highly valuable for water resources planning and management. The uncertainty of runoff forecasting is also essential for water resource managers. However, deterministic models only provide single prediction values without uncertainty attached. In this study, Gaussian Mixture Regression (GMR) approach is applied for probabilistic middle and long-term runoff forecasting, which can quantify the predictive uncertainty directly. GMR consists two parts, optimizing the model parameters and hyperparameters of Gaussian Mixture Model (GMM) and forecasting the posterior conditional probability density. GMR is applied to a real-world runoff forecasting case study at Xiangjiaba Station and Panzhihua Station on the Jinsha River. And it is compared with Support Vector Machines and Artificial Neural Network. The experimental results show its excellent performance both on accuracy and reliability. Uncertainty estimation for the probabilistic forecast is also shown, the results demonstrate that GMR is able to handle the heteroscedastic data like runoff and can provide an effective uncertainty estimation.

Suggested Citation

  • Yongqi Liu & Lei Ye & Hui Qin & Shuo Ouyang & Zhendong Zhang & Jianzhong Zhou, 2019. "Middle and Long-Term Runoff Probabilistic Forecasting Based on Gaussian Mixture Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1785-1799, March.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:5:d:10.1007_s11269-019-02221-y
    DOI: 10.1007/s11269-019-02221-y
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    References listed on IDEAS

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    1. Yongqi Liu & Hui Qin & Li Mo & Yongqiang Wang & Duan Chen & Shusen Pang & Xingli Yin, 2019. "Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 337-354, January.
    2. Jiang, Zhiqiang & Li, Rongbo & Li, Anqiang & Ji, Changming, 2018. "Runoff forecast uncertainty considered load adjustment model of cascade hydropower stations and its application," Energy, Elsevier, vol. 158(C), pages 693-708.
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    Cited by:

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    2. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    3. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

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