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A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting

Author

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  • Xinxin He

    (Xi’an University of Technology)

  • Jungang Luo

    (Xi’an University of Technology)

  • Peng Li

    (Xi’an University of Technology)

  • Ganggang Zuo

    (Xi’an University of Technology)

  • Jiancang Xie

    (Xi’an University of Technology)

Abstract

Accurate and reliable monthly runoff forecasting is of great significance for water resource optimization and management. A neoteric hybrid model based on variational mode decomposition (VMD) and gradient boosting regression (GBRT) called VMD-GBRT was proposed and applied for monthly runoff forecasting. VMD was first employed to decompose the original monthly runoff series into several intrinsic mode functions (IMFs). The optimal number of input variables were then chosen according to the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The trained GBRT model was used as a forecasting instrument to predict the testing set of each normalized subsequence. The ensemble forecasting result was finally generated by aggregating the prediction results of all subsequences. The proposed hybrid model was evaluated using an original monthly runoff series, from 1/1969 to 12/2018, measured at the Huaxian, Lintong and Xianyang hydrological stations in the Wei River Basin (WRB), China. The EEMD-GBRT, the single GBRT, and the single SVM were adopted as comparative forecast models using the same dataset. The results indicated that the VMD-GBRT model exhibited the best forecasting performance among all the peer models in terms of the coefficient of determination (R2 = 0.8840), mean absolute percentage error (MAPE = 19.7451), and normalized root-mean-square error (NRMSE = 0.3468) at Huaxian station. Furthermore, the model forecasting results applied at Lintong and Xianyang stations were consistent with those at Huaxian station. This result further verified the accuracy and stability of the VMD-GBRT model. Thus, the proposed VMD-GBRT model was effective method for forecasting non-stationary and non-linear runoff series, and can be recommended as a promising model for monthly runoff forecasting.

Suggested Citation

  • Xinxin He & Jungang Luo & Peng Li & Ganggang Zuo & Jiancang Xie, 2020. "A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 865-884, January.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:2:d:10.1007_s11269-020-02483-x
    DOI: 10.1007/s11269-020-02483-x
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    1. Chongli Di & Xiaohua Yang & Xiaochao Wang, 2014. "A Four-Stage Hybrid Model for Hydrological Time Series Forecasting," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-18, August.
    2. Landry, Mark & Erlinger, Thomas P. & Patschke, David & Varrichio, Craig, 2016. "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1061-1066.
    3. Mohammed Falah Allawi & Ahmed El-Shafie, 2016. "Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4773-4788, October.
    4. Umut Okkan & Zafer Ali Serbes, 2012. "Rainfall–runoff modeling using least squares support vector machines," Environmetrics, John Wiley & Sons, Ltd., vol. 23(6), pages 549-564, September.
    5. Lei Zou & Jun Xia & Dunxian She, 2018. "Analysis of Impacts of Climate Change and Human Activities on Hydrological Drought: a Case Study in the Wei River Basin, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1421-1438, March.
    6. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    7. Lahmiri, Salim, 2015. "Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 130-138.
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