Application of a Combination Model Based on Wavelet Transform and KPLS-ARMA for Urban Annual Water Demand Forecasting
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 8
Abstract
A combination of models including wavelet transform and kernel partial least squares-autoregressive moving average (KPLS-ARMA) is proposed to explore the nonstationarity of the urban annual water demand series, the nonlinear relationships between water demand series and its determinants, and the high correlations among those determinants, based on which a novel forecast model is proposed for urban annual water demand. First, by Mallat algorithm, a nonstationary urban annual water demand series is decomposed and reconstructed into one low-frequency component and one or several high-frequency components. Following that, the kernel partial least squares (KPLS) model is applied to simulating the low-frequency component. An autoregressive moving average (ARMA) model is constructed for each of the high-frequency components. The combined models are applied to understanding the nonstationarity and forecasting the annual water demand of Dalian City. The results are then compared with those from other several methods. It is shown that the proposed method, which combines advanced statistical tools (such as wavelet transform and artificial intelligence) and traditional statistical models, provides the most accurate forecast of urban annual water demand in the city.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was supported by the National Basic Research Program of China (Grant No. 2013CB036400), and the National Natural Science Foundation of China (Grant No. 51279021 and 51079014). We are indebted to the editors and reviewers for their valuable comments and suggestions. Especially, we wish to thank Prof. Ximing Cai for his constructive suggestions used to improve the quality of the manuscript.
References
Adamowski, J., Chan, H. F., Prasher, S. O., Ozga-Zielinski, B., and Sliusarieva, A. (2012). “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 Resour. Res., 48(1), 1–14.
Azadeh, A., Neshat, N., and Hamidipour, H. (2012). “Hybrid fuzzy regression–artificial neural network for improvement of short-term water consumption estimation and forecasting in uncertain and complex environments: Case of a large metropolitan city.” J. Water Resour. Plann. Manage., 71–75.
Beal, C., and Stewart, R. (2013). “Identifying residential water end-uses underpinning peak day and peak hour demand.” J. Water Resour. Plann. Manage., 04014008.
Blokker, E. J. M., Pieterse-Quirijns, E. J., Vreeburg, J. H. G., and van Dijk, J. C. (2011). “Simulating nonresidential water demand with a stochastic end-use model.” J. Water Resour. Plann. Manage., 511–520.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time series analysis: Forecasting and control, 3rd Ed., Prentice Hall, Englewood Cliffs, New Jersey.
Canu, S., Sobral, R., and Lengelle, R. (1990). “Formal neural network as an adaptive model for water demand.” Proc., Int. Conf. on Neural Network, IEEE Press, New Jersey, 131–136.
Chang, J. X., Yu, X. J., Huang, Q., and Liu, Z. (2008). “Water demand forecasting based on multi-scales analysis and neural network.” Comput. Eng. Appl., 44(17), 219–221.
Dash, R., Paramguru, R. L., and Dash, R. (2011). “Comparative analysis of supervised and unsupervised discretization techniques.” Int. J. Adv. Sci. Technol., 2(3), 29–37.
Donkor, E., Mazzuchi, T., Soyer, R., and Roberson, J. (2014). “Urban water demand forecasting: A review of methods and models.” J. Water Resour. Plann. Manage., 146–159.
Du, F. X., and Xu, G. (2008). “Application of partial least squares method to forecast the urban water demand quantity.” Water Power, 34(6), 20–23.
Duan, K., Keerthi, S. S., and Poo, A. N. (2003). “Evaluation of simple performance measures for tuning SVM hyperparameters.” Neurocomputing, 51, 41–59.
Ghiassi, M., David, K., and Saidane, H. (2008). “Urban water demand forecasting with a dynamic artificial neural network model.” J. Water Resour. Plann. Manage., 138–146.
Jain, A., Varshney, A. K., and Joshi, U. C. (2001). “Short-Term water demand forecast modelling at IIT Kanpur using artificial neural networks.” Water Resour. Manage., 15(5), 299–321.
Jalali-Heravi, M., and Kyani, A. (2007). “Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: Activity of carbonic anhydrase II inhibitors.” Eur. J. Med. Chem., 42(5), 649–659.
Lawgali, F. F. (2008). “Forecasting water demand for agricultural, industrial and domestic use in Libya.” Int. Rev. Bus. Res. Pap., 4(5), 231–248.
Lenhart, T., Eckhardt, K., Fohrer, N., and Frede, H.-G. (2002). “Comparison of two different approaches of sensitivity analysis.” Phys. Chem. Earth , 27(9–10), 645–654.
Lewi, P. J. (1995). “Pattern recognition, reflection from a chemometric point of view.” Chemom. Intell. Lab. Syst., 28(1), 23–33.
Li, Z. N., and Ye, A. Z. (2000). Advanced econometrics, Tsinghua University Press, Beijing.
Liu, J. Q., Cheng, W. P., and Zhang, T. Q. (2013). “Principal factor analysis for forecasting diurnal water-demand pattern using combined rough-set and fuzzy-clustering technique.” J. Water Resour. Plann. Manage., 23–33.
Liu, L., Zhou, J. Z., Li, Y. H., and Zhang, Y. C. (2009). “Chaotic prediction model for runoff using wavelet de-noising.” J. Huazhong Univ. of Sci. Tech., 37(7), 86–89.
Luo, X. L., and Yang, J. Q. (2013). “Study on the imbalance of shipping demand and supply of inland water transportation of Yangtze River.” Proc., ICTIS 2013, ASCE Press, Reston, Virginia, 2211–2218.
Maidment, D. R., and Parzen, E. (1984a). “Cascade model of monthly municipal water use.” Water Resour. Res., 20(1), 15–23.
Maidment, D. R., and Parzen, E. (1984b). “Time patterns of water use in six Texas cities.” J. Water Resour. Plann. Manage., 90–106.
Mallat, S. (1999). A wavelet tour of signal processing, 2nd Ed., Academic Press, San Diego.
Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J.-M. (1996). Wavelet toolbox: For use with Matlab, The MathWorks, Natick, MA.
Msiza, I. S., Nelwamondo, F. V., and Marwala, T. (2007). “Artificial neural networks and support vector machines for water demand time series forecasting.” Proc., Int. Conf. on Systems, Man and Cybernetics, IEEE Press, New Jersey, 638–643.
Odan, F. K., and Reis, L. F. (2012). “Hybrid water demand forecasting model associating artificial neural network with Fourier series.” J. Water Resour. Plann. Manage., 245–256.
Polebitski, A., and Palmer, R. (2010). “Seasonal residential water demand forecasting for census tracts.” J. Water Resour. Plann. Manage., 27–36.
Qin, J. X., Wang, L., Huang, G. R., and Fan, Q. F. (2012). “Water demand prediction in northern Gulf economic zone of Guangxi province based on system dynamics.” Water Resour. Power, 30(2), 28–31.
Quantitative Micro software, LLC. (2007). Eviews 6 user’s guide, Quantitative Micro software, LLC, Irvine CA.
Rosipal, R., and Trejo, L. J. (2001). “Kernel partial least squares regression in reproducing kernel Hilbert Space.” J. Mach. Learn. Res., 2(12), 97–123.
Said, E. S., and Dickey, D. A. (1984). “Testing for unit roots in autoregressive-moving average models of unknown order.” Biometrika, 71(3), 599–607.
Skowron, A., Bazan, J. G., and Latkowski, R. (2005). RSES 2.2 user’s guide, Institute of Mathematics, Warsaw Univ., Warsaw, Poland.
Tiwari, M. K., and Chatterjee, C. (2010). “Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach.” J. Hydrol., 394(3–4), 458–470.
Tong, C. F., Shi, H. B., Bao, X. Q., and Li, H. P. (2011). “Application of a combined model based on wavelet analysis for predicting crop water requirement.” T. Chin. Soc. Agr. Eng., 27(5), 93–98.
Tsai, C. J., Lee, C. I., and Yang, W. P. (2008). “A discretization algorithm based on class-attribute contingency coefficient.” Inf. Sci., 178(3), 714–731.
Wang, J. P., and Sun, D. S. (2006). Modern data analysis, China Machine Press, Beijing.
Wu, L. (2009). “The study on several important problems in urban water resources system.” Doctoral thesis, Dalian Univ. of Technology, Dalian, China.
Xu, Y. S., Mei, Y. D., and Yong, T. (2011). “Combined forecasting model of urban water demand under changing environment.” Proc., 2011 Int. Conf. on Electric Technology and Civil Engineering (ICETCE), IEEE Press, New Jersey, 1103–1107.
Zhang, Y. W., Teng, Y. D., and Zhang, Y. (2010). “Complex process quality prediction using modified kernel partial least squares.” Chem. Eng. Sci., 65(6), 2153–2158.
Zhong, Q. (2008). “Exploring large systems: Neurons and genes.” Doctoral thesis, Univ. of California at Los Angeles, Los Angeles.
Zhou, D., Zhang, R. Q., Liu, L. M., Gao, L. L., and Cai, S. M. (2009). “A study on water resources consumption by principal component analysis in Qingtongxia irrigation areas of Yinchuan Plain, China.” J. Food Agric. Environ., 7(3–4), 734–738.
Zhou, H. C., Peng, Y., and Liang, G. H. (2008). “The research of monthly discharge predictor- corrector model based on wavelet decomposition.” Water Resour. Manage., 22(2), 217–227.
Zhu, Z. Y., Ouyang, T. P., Deng, Q. L., Zhou, H. Y., and Kuang, Y. Q. (2004). “Forecasting water demand and supply in South China.” J. Am. Waterworks Assoc., 96(8), 159–172.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 8 • August 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: May 11, 2012
Accepted: Jul 31, 2013
Published online: Aug 2, 2013
Published in print: Aug 1, 2014
Discussion open until: Sep 8, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.