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A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

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Abstract

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

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Acknowledgements

This study was supported by the National Key Research and Development Program of China (Grant Nos. 2016YFC0402706, 2016YFC0402707, and 2016YFC0402709). We gratefully acknowledge the anonymous editors and reviewers for their insightful and professional comments, which greatly improved this manuscript.

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Authors and Affiliations

  1. College of Hydrology and Water Resources, Hohai University, No.1 Xikang Road, Gulou District, Nanjing City, Jiangsu Province, China

    Zhongmin Liang, Yujie Li, Yiming Hu, Binquan Li & Jun Wang

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  1. Zhongmin Liang
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  2. Yujie Li
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Correspondence to Zhongmin Liang.

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Liang, Z., Li, Y., Hu, Y. et al. A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework. Theor Appl Climatol 133, 137–149 (2018). https://doi.org/10.1007/s00704-017-2186-6

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