Hourly Campus Water Demand Forecasting Using a Hybrid EEMD-Elman Neural Network Model

  • Xiao Deng
  • Shuai Hou
  • Wen-zhu Li
  • Xin LiuEmail author
Conference paper
Part of the Environmental Earth Sciences book series (EESCI)


Accurate and reliable water demand forecasting is important for effective and sustainable planning and use of water supply infrastructures. In this paper, a hybrid EEMD-Elman neural network model for hourly campus water demand forecast is proposed, aiming at improving the accuracy and reliability of water demand forecast. The proposed method combines the Elman neural network, EEMD method, and phase space reconstruction method providing favorable dynamic forecast characteristics and improving the forecasting accuracy and reliability. Simulation results show that the proposed model provides a better performance of hourly campus water demand forecast by using the real data of water usage of our campus.


Elman neural networks EEMD Phase space reconstruction Water demand forecasting 


  1. 1.
    Qin, T.L., et al.: Water demand forecast in the Baiyangdian basin with the extensive and low-carbon economic modes. J. Appl. Math. 2014, 1–10 (2014). Scholar
  2. 2.
    Beal, C.D.: SEQ residential end use study. J. Aust. Water Assoc. 38(1), 80–84 (2011).
  3. 3.
    Fielding, K.S., et al.: Using individual householder survey responses to predict household environmental outcomes: the cases of recycling and water conservation. Resour. Conserv. Recycl. 106, 90–97 (2016). Scholar
  4. 4.
    Shen, J.-C., et al.: Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method. Stoch. Env. Res. Risk Assess. 29(7), 1903–1920 (2015). Scholar
  5. 5.
    Luo, X., Jiaqi Yang, J.: Study on the imbalance of shipping demand and supply of inland water transportation of Yangtze River. ICTIS 2013: Improving Multimodal Transportation Systems-Information, Safety, and Integration, p. 2211–2221 (2013).
  6. 6.
    Adamowski, J., Chan, H.-F., Prasher, S.O.: Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Res. Res. 48(1) (2012).
  7. 7.
    Mombeni, H.A., et al.: Estimation of water demand in Iran based on SARIMA models. Environ. Model. Assess. 18(5), 559–565 (2013). Scholar
  8. 8.
    Braun, M., et al.: 24-hours demand forecasting based on SARIMA and support vector machines. Procedia Eng. 89, 926–933 (2014). Scholar
  9. 9.
    Mombeni, H.A., et al.: Reducing water consumption after targeted subsidy plan in Iran. Water Resour. 42(3), 389–396 (2015). Scholar
  10. 10.
    Vijayalaksmi, D.P., Jinesh Babu, K.S.: Water supply system demand forecasting using adaptive neuro-fuzzy inference system. Aquat. Procedia 4, 950–956 (2015). Scholar
  11. 11.
    Candelieri, A., Archetti, F.: Identifying typical urban water demand patterns for a reliable short-term forecasting—the icewater project approach. Procedia Eng. 89, 1004–1012 (2014). Scholar
  12. 12.
    Adamowski, J.F.: Peak daily water demand forecast modeling using artificial neural networks. J. Water Res. Plann. Manage. 134(2), 119–128 (2008). Scholar
  13. 13.
    Bennett, C., Stewart, R.A., Beal, C.D.: ANN-based residential water end-use demand forecasting model. Expert Syst. Appl. 40(4), 1014–1023 (2013). Scholar
  14. 14.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986). Scholar
  15. 15.
    Jia, W., et al.: Study on optimized Elman neural network classification algorithm based on PLS and CA. Comput. Intell. Neurosci. 2014, 724317 (2014). Scholar
  16. 16.
    Wu, Z.H., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1–41 (2009). Scholar
  17. 17.
    Wang, J., Zhang, W., Li, Y., Wang, J., Dang, Z.: Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl. Soft Comput. 23, 452–459 (2014). Scholar
  18. 18.
    Lin, C.-M., Boldbaatar, E.-A.: Autolanding control using recurrent wavelet Elman neural network. IEEE Trans. Syst. Man Cybern. Syst. 45(9), 1281–1291 (2015).
  19. 19.
    Kim, H.S., Eykholt, R., Salas, J.D.: Nonlinear dynamics, delay times and embedding windows. Phys. D 127, 48–60 (1999).

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information and Electric EngineeringHebei University of EngineeringHandanChina

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