Abstract
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.
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References
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). https://doi.org/10.1155/2014/6734
Beal, C.D.: SEQ residential end use study. J. Aust. Water Assoc. 38(1), 80–84 (2011). https://www.researchgate.net/publication/279662589)
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). https://doi.org/10.1016/j.resconrec.2015.11.009
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). https://doi.org/10.1007/s00477-015-1074-9
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). https://doi.org/10.1061/9780784413036.297
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). https://doi.org/10.1029/2010wr009945
Mombeni, H.A., et al.: Estimation of water demand in Iran based on SARIMA models. Environ. Model. Assess. 18(5), 559–565 (2013). https://doi.org/10.1007/s10666-013-9364-4
Braun, M., et al.: 24-hours demand forecasting based on SARIMA and support vector machines. Procedia Eng. 89, 926–933 (2014). https://doi.org/10.1016/j.proeng.2014.11.526
Mombeni, H.A., et al.: Reducing water consumption after targeted subsidy plan in Iran. Water Resour. 42(3), 389–396 (2015). https://doi.org/10.1134/S0097807815030100
Vijayalaksmi, D.P., Jinesh Babu, K.S.: Water supply system demand forecasting using adaptive neuro-fuzzy inference system. Aquat. Procedia 4, 950–956 (2015). https://doi.org/10.1016/j.aqpro.2015.02.119
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). https://doi.org/10.1016/j.proeng.2014.11.218
Adamowski, J.F.: Peak daily water demand forecast modeling using artificial neural networks. J. Water Res. Plann. Manage. 134(2), 119–128 (2008). https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(119)
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). https://doi.org/10.1016/j.eswa.2012.08.012
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986). https://doi.org/10.1038/323533a0
Jia, W., et al.: Study on optimized Elman neural network classification algorithm based on PLS and CA. Comput. Intell. Neurosci. 2014, 724317 (2014). https://doi.org/10.1155/2014/724317
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). https://doi.org/10.1142/S1793536909000047
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). https://doi.org/10.1016/j.asoc.2014.06.027
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). https://doi.org/10.1109/tsmc.2015.2389752
Kim, H.S., Eykholt, R., Salas, J.D.: Nonlinear dynamics, delay times and embedding windows. Phys. D 127, 48–60 (1999). https://doi.org/10.1016/s0167-2789(98)00240-1
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Deng, X., Hou, S., Li, Wz., Liu, X. (2019). Hourly Campus Water Demand Forecasting Using a Hybrid EEMD-Elman Neural Network Model. In: Dong, W., Lian, Y., Zhang, Y. (eds) Sustainable Development of Water Resources and Hydraulic Engineering in China. Environmental Earth Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-61630-8_7
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DOI: https://doi.org/10.1007/978-3-319-61630-8_7
Publisher Name: Springer, Cham
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