Application of a combination model based on wavelet transform and KPLS-ARMA for urban annual water demand forecasting

Journal of Water Resources Planning and Management

Volumn 140, Issue 8, 2014, Pages

  Huang, Lili ^a   Zhang, Chi ^a   Peng, Yong ^a   Zhou, Huicheng ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Annual water demand; Autoregressive and moving average model (ARMA); Combination forecasting; Kernel partial least squares; Nonstationary time series; Wavelet transform

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; GAUSSIAN NOISE (ELECTRONIC); LEAST SQUARES APPROXIMATIONS; STATISTICAL MECHANICS; WAVELET TRANSFORMS;

AUTOREGRESSIVE AND MOVING AVERAGE MODELS; COMBINATION FORECASTING; KERNEL PARTIAL LEAST SQUARES; NON-STATIONARY TIME SERIES; WATER DEMAND;

AUTOREGRESSIVE MOVING AVERAGE MODEL;

DEMAND ANALYSIS; FORECASTING METHOD; LEAST SQUARES METHOD; TIME SERIES; TRANSFORM; WATER MANAGEMENT; WATER SUPPLY; WAVELET ANALYSIS;

CHINA; DALIAN; LIAONING;

EID: 84928648158     PISSN: 07339496     EISSN: 19435452     Source Type: Journal    
DOI: 10.1061/(ASCE)WR.1943-5452.0000397     Document Type: Article

References (44)

1. 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.
2. 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. 10.1061/(ASCE)WR.1943-5452.0000152.
3. Beal, C., and Stewart, R. (2013). "Identifying residential water end-uses underpinning peak day and peak hour demand". J. Water Resour. Plann. Manage. 04014008. 10.1061/(ASCE)WR.1943-5452.0000357.
4. 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. 10.1061/(ASCE)WR.1943-5452.0000146.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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. 10.1061/(ASCE)WR.1943-5452.0000314.
10. 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.
11. Duan, K., Keerthi, S. S., and Poo, A. N. (2003). "Evaluation of simple performance measures for tuning SVM hyperparameters". Neurocomputing, 51, 41-59.
12. 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. 10.1061/(ASCE)0733-9496(2008)134:2(138).
13. 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.
14. 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.
15. Lawgali, F. F. (2008). "Forecasting water demand for agricultural, industrial and domestic use in Libya". Int. Rev. Bus. Res. Pap., 4(5), 231-248.
16. 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.
17. Lewi, P. J. (1995). "Pattern recognition, reflection from a chemometric point of view". Chemom. Intell. Lab. Syst., 28(1), 23-33.
18. Li, Z. N., and Ye, A. Z. (2000). Advanced econometrics, Tsinghua University Press, Beijing.
19. 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. 10.1061/(ASCE)WR.1943-5452.0000223.
20. 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.
21. 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.
22. Maidment, D. R., and Parzen, E. (1984). "Cascade model of monthly municipal water use". Water Resour. Res., 20(1), 15-23.
23. Maidment, D. R., and Parzen, E. (1984). "Time patterns of water use in six Texas cities". J. Water Resour. Plann. Manage. 90-106. 10.1061/(ASCE)0733-9496(1984)110:1(90).
24. Mallat, S. (1999). A wavelet tour of signal processing, 2nd Ed., Academic Press, San Diego.
25. Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J.-M. (1996). Wavelet toolbox: For use with Matlab, The MathWorks, Natick, MA.
26. 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.
27. 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. 10.1061/(ASCE)WR.1943-5452.0000177.
28. Polebitski, A., and Palmer, R. (2010). "Seasonal residential water demand forecasting for census tracts". J. Water Resour. Plann. Manage. 27-36. 10.1061/(ASCE)WR.1943-5452.0000003.
29. 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.
30. Quantitative Micro software, LLC. (2007). Eviews 6 user's guide, Quantitative Micro software, LLC, Irvine CA.
31. 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.
32. 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.
33. Skowron, A., Bazan, J. G., and Latkowski, R. (2005). RSES 2.2 user's guide, Institute of Mathematics, Warsaw Univ., Warsaw, Poland.
34. 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.
35. 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.
36. 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.
37. Wang, J. P., and Sun, D. S. (2006). Modern data analysis, China Machine Press, Beijing.
38. Wu, L. (2009). "The study on several important problems in urban water resources system". Doctoral thesis, Dalian Univ. of Technology, Dalian, China.
39. 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.
40. 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.
41. Zhong, Q. (2008). "Exploring large systems: Neurons and genes". Doctoral thesis, Univ. of California at Los Angeles, Los Angeles.
42. 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.
43. 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.
44. 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.