Research on Hydrological Time Series Prediction Based on Combined Model

  • Yi ChengEmail author
  • Yuansheng Lou
  • Feng Ye
  • Ling Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 727)


Water level prediction of river runoff is an important part of hydrological forecasting. The change of water level not only has the trend and seasonal characteristics, but also contains the noise factors. And the water level prediction ability of a single model is limited. Since the traditional ARIMA (Autoregressive Integrated Moving Average) model is not accurate enough to predict nonlinear time series, and the WNN (Wavelet Neural Network) model requires a large training set, we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition. The combined model fit the wavelet transform sequences whose frequency are high with the WNN, and the scale transform sequence which has low frequency is fitted by the ARIMA model, and then the prediction results of the above are reconstructed by wavelet transform. The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead. The combined model is compared with other single models with MATLAB, and the experimental results show that the accuracy of the combined model is improved by 7% compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.


Combined model Autoregressive Integrated Moving Average Prediction Wavelet neural network Hydrological time series 



This work is supported by (1) National Natural Science Foundation of China (61300122); (2) A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; (3) Water Science and Technology Project of Jiangsu Province (2013025)


  1. 1.
    Rahimi, K.A.: Artificial neural network estimation of reference evapo transpiration from pan evaporation in a semi-arid environment. Irrig. Sci. 27, 35–39 (2008)CrossRefGoogle Scholar
  2. 2.
    Zhang, Q., Benveniste, A.: Wavelet networks. IEEE Trans. Neural Netw. 3, 889–898 (1992)CrossRefGoogle Scholar
  3. 3.
    Chen, Z., Feng, T.: Research progress and prospect of wavelet neural network. J. Ocean Univ. Qingdao 04, 663–668 (1999)Google Scholar
  4. 4.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time series analysis: forecasting and control. J. Oper. Res. Soc. 22(2), 199–201 (1976)Google Scholar
  5. 5.
    Zheng, P.: The combined forecasting based on ARIMA model. In: Yanshan University (2009)Google Scholar
  6. 6.
    Bates, J.M., Granger, C.W.J.: The combination of forecasts. J. Oper. Res. Soc. 20(4), 451–468 (1969)CrossRefGoogle Scholar
  7. 7.
    Mo, D.: The application of ARIMA and BP neural network mixture model in the GDP estimation of Guangxi. J. Guangxi Univ. Financ. Econ. 24(6), 31–34 (2011)Google Scholar
  8. 8.
    Zhai, J., Cao, J.: Combined forecasting model based on time series ARIMA and BP neural network. Stat. Decis. 4, 29–32 (2016)Google Scholar
  9. 9.
    Dang, G.: Research on combined forecast method in short-term load forecasting. In: Hunan University (2011)Google Scholar
  10. 10.
    Zhang, P., Li, X.: Based on wavelet neural network and ARMA model of CPI fluctuation range forecast. Inf. Technol. 2, 131–135 (2016)Google Scholar
  11. 11.
    Zheng, J.: The research on forecasting of financial time series based on wavelet analysis and neural network. In: Xiamen university (2009)Google Scholar
  12. 12.
    Yang, Q., Zhang, J., Li, W., et al.: Prediction of wind speed and wind power generation based on Wavelet Neural Network. Power Grid Technol. 17, 44–48 (2009)Google Scholar
  13. 13.
    Wei, S., Yang, H., Song, J., et al.: A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrol. Sci. J. 58(2), 374–389 (2013)CrossRefGoogle Scholar
  14. 14.
    Falamarzi, Y., Palizdan, N., Huang, Y.F., et al.: Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agric. Water Manag. 140(3), 26–36 (2014)CrossRefGoogle Scholar
  15. 15.
    Zhu, Y., Li, S., Fan, Q., et al.: Wavelet neural network model based complex hydrological time series prediction. J. Shandong Univ. (Eng. Sci.) 04, 119–124 (2011)Google Scholar
  16. 16.
    Nourani, V., Özgür, K., Komasi, M.: Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. J. Hydrol. 402(1–2), 41–59 (2011)CrossRefGoogle Scholar
  17. 17.
    Gao, Z., Yu, X.: Wavelet Analysis and Application in MATLAB, 3rd edn. National Defense Industry Press, Beijing (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.College of Computer and InformationHohai UniversityNanjingChina

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