A multi-scale relevance vector regression approach for daily urban water demand forecasting

Journal of Hydrology

Volumn 517, Issue , 2014, Pages 236-245

  Bai, Yun ^a   Wang, Pu ^a   Li, Chuan ^b   Xie, Jingjing ^c   Wang, Yin ^a  

^a CHONGQING UNIVERSITY   (China)

^b CHONGQING TECHNOLOGY AND BUSINESS UNIVERSITY   (China)

^c CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Adaptive chaos particle swarm optimization; Multi scale; Relevance vector regression; Stationary wavelet transform; Water demand forecasting

Indexed keywords

CHAOS PARTICLE SWARM OPTIMIZATIONS; MULTI-SCALE; RELEVANCE VECTOR REGRESSION; STATIONARY WAVELET TRANSFORMS; WATER DEMAND FORECASTING;

DATA ACQUISITION; FORECASTING METHOD; REGRESSION ANALYSIS; TIME SERIES; WATER DEMAND; WATER SUPPLY; WAVELET; ALGORITHM; DATA SET; OPTIMIZATION;

EID: 84904904412     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2014.05.033     Document Type: Article

References (45)

1. Adamowski J.F. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J. Hydrol. 2008, 353(3-4):247-266.
2. Adamowski J., Karapataki C. Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. J. Hydrol. Eng. 2010, 15(10):729-743.
3. Alhumoud J.M. Freshwater consumption in Kuwait: analysis and forecasting. J. Water Supply Res. Technol.-AQUA 2008, 57(4):279-288.
4. Babel M.S., Shinde V.R. Identifying prominent explanatory variables for water demand prediction using artificial neural networks: a case study of Bangkok. Water Resour. Manage. 2011, 25:1653-1676.
5. Beal C.D., Stewart R.A., Fielding K. A novel mixed method smart metering approach to reconciling differences between perceived and actual residential end use water consumption. J. Cleaner Prod. 2011, 10.1016/j.jclepro.09.007.
6. Billings, B., Jones, C., 2008. Forecasting Urban Water Demand, second ed. American Waterworks Association.
7. Cai J., Li Q., Li L., Peng H., Yang Y. A hybrid CPSO-SQP method for economic dispatch considering the valve-point effects. Energy Convers. Manage. 2012, 53(1):175-181.
8. Caiado J. Performance of combined double seasonal univariate time series models for forecasting water demand. J. Hydrol. Eng. 2010, 15(3):215-222.
9. Campisi-Pinto S., Adamowski J., Oron G. Forecasting urban water demand via wavelet-denoising and neural network models, case study: city of Syracuse, Italy. Water Resour. Manage. 2012, 26(12):3539-3558.
10. Cutore P., Campisano A., Kapelan Z., Modica C., Savic D. Probabilistic prediction of urban water consumption using the SCEM-UA algorithm. Urban Water J. 2008, 5(2):125-132.
11. Donkor E.A., Mazzuchi T.A., Soyer R., Roberson J.A. Urban water demand forecasting : a review of methods and models. J. Water Resour. Plan. Manage. 2012, 10.1061/(ASCE)WR.1943-5452.0000314.
12. Fan J., Liu M., Hai Y., Wang L. GUI for computing center-frequency of the scale and wavelet functions and its applications. Sci. Technol. Rev. 2007, 25(24):36-39.
13. Firat M., Turan M.E., Yurdusev M.A. Comparative analysis of neural network techniques for predicting water consumption time series. J. Hydrol. 2010, 384(1-2):46-51.
14. Herrera M., Torgo L., Izquierdo J., Pe´rez-Garci´a R. Predictive models for forecasting hourly urban water demand. J. Hydrol. 2010, 387(1-2):141-150.
15. Holzfuss J., Mayer-Kress G. An approach to error-estimation in the application of dimension algorithms. Dimensions Entropies Chaot. Syst. 1986, 32:114-122.
16. Kennedy, J., Eberhart, R.C., 1995. A new optimizer using particles swarm theory. In: Proceedings of Sixth International Symposium on Micro Machine and Human Science, pp. 39-43.
17. Kiran M.S., O¨zceylan E., Gu¨ndu¨z M., Paksoy T. A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Convers. Manage. 2012, 53(1):75-83.
18. Kugiumtzis D. State space reconstruction parameters in the analysis of chaotic time series the role of the time window length. Physics D 1996, 95(1):13-28.
19. Kuo R.J., Hong S.Y., Huang Y.C. Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. Appl. Math. Model. 2010, 34(12):3976-3990.
20. Lee S., Wentz E., Gober P. Space-time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona. Stoch. Environ. Res. Risk Assess. 2010, 24(2):283-295.
21. Li, Z., Chen, G., 2010. Integration of fuzzy logic and chaos theory, first ed. Springer Publishing Company.
22. Li C., Liang M. Separation of vibration-induced signal of oil debris sensor for vibration monitoring. Smart Mater. Struct. 2011, 20(4):045016.
23. Li C., Wang S. Next-day power market clearing prince forecasting using artificial fish-swarm based neural network. Lect. Notes Comput. Sci. 2006, 3972:1290-1295.
24. Li C., Liang M., Wang Y., Dong Y. Vibration suppression using two-terminal flywheel. Part I: Modeling and characterization. J. Vib. Control 2012, 18(8):1096-1105.
25. Li, C., Wang, J., Wang, H., Zhang, S., 2012b. Urban water consumption long-term prediction model based on the water price elasticity in China. World Environmental and Water Resources Congress 2012: Crossing Boundaries, pp. 2491-2500.
26. Mabrouk A.B., Abdallah N.B., Dhifaoui Z. Wavelet decomposition and autoregressive model for time series prediction. Appl. Math. Comput. 2008, 199(1):334-340.
27. Maheswaran R., Khosa R. Multiscale nonlinear model for monthly stream flow forecasting: a wavelet based approach. J. Hydroinform. 2012, 14(2):424-442.
28. Mitani, Y., Tsutsumoto, K., Kagawa, N., 2003. Time series prediction of acoustic signals using neural network model and wavelet shrinkage. In: Proceedings of the tenth International Congress on Sound and Vibration, pp. 4189-4196.
29. Mohamed M., Al-Mualla A. Water demand forecasting in Umm Al-Quwain (UAE) using the IWR-MAIN specify forecasting model. Water Resour. Manage. 2010, 24(14):4093-4120.
30. Nasseri M., Moeini A., Tabesh M. Forecasting monthly urban water demand using extended Kalman filter and genetic programming. Expert Syst. Appl. 2011, 38:7387-7395.
31. Odan F.K., Reis L.F.R. Hybrid water demand forecasting model associating artificial neural network with Fourier series. J. Water Resour. Plan. Manage. 2012, 138:245-256.
32. Oshima N., Kosuda T. Distribution reservoir control with demand prediction using deterministic-chaos method. Water Sci. Technol. 1998, 37(12):389-395.
33. Shi Y., Eberhart R. A modified particle swarm optimizer. Evolut. Comput. Proc. 1998, 69-73.
34. Soltani S. On the use of the wavelet decomposition for time series prediction. Neurocomputing 2002, 48:267-277.
35. Steeb, W.H., 2011. The Nonlinear Workbook, fifth ed. World Scientific.
36. Steinhauser, M.O., 2008. Computational Multiscale Modeling of Fluids and Solids: Theory and Applications, fitst ed. Springer Publishing Company.
37. Tabesh M., Dini M. Fuzzy and neuro-fuzzy models for short-term water demand forecasting in Tehran. Iran. J. Sci. Technol., Trans. B 2009, 33:61-77.
38. Ticlavilca A.M., Mckee M., Walker W.R. Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model. Irrig. Sci. 2013, 31(2):151-167.
39. Tipping M.E. The relevance vector machine. Adv. Neur. Inform. Process. Syst. 2000, 12:652-658.
40. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 2001, 1:211-244.
41. Wei S., Lei A., Islam S. Modeling and simulation of industrial water demand of Beijing municipality in China. Front. Environ. Sci. Eng. China 2010, 4(1):91-101.
42. Weinan E., Engquist B. Multiscale modeling and computation. Notices Am. Math. Soc. 2003, 50(9):1062-1070.
43. Wong J.S., Zhang Q., Chen Y.D. Statistical modeling of daily urban consumption in Hong Kong: trend, changing patterns, and forecast. Water Resour. Res. 2010, 46(3).
44. Zhao P., Zhang H. Chaotic characters and forecasting of urban water consumption. China Water Wastewater 2008, 24(5). pp. 90-93, 97.
45. Ziervogel G., Johnston P., Matthew M., Mukheibir P. Using climate information for supporting climate change adaptation in water resource management in South Africa. Climat. Change 2010, 103:537-554.