Paper Titles

Hydraulic Experimental Study on Two Kinds of Nonmetallic Plastic Pipes
p.2014

The Analytical Solution of Hydrodynamic Pressure of Full Water Tunnel in the Horizontal Excitation
p.2018

The Application of Three Dimensional Drainage Flexible and Ecological Reinforced Construction in High-Speed Railway Station Roadbed
p.2025

Simplified Technology for Modified Micro-Irrigation of Agricultural Sheds
p.2030

Research on Urban Water Demand Prediction
p.2037

Research on Identification of Comprehensive Pipeline Headroom in Certain Area
p.2041

Magnetic Water Treatment to Inhibit Calcium Carbonate Scale Deposition in the Drainage System of an Old Tunnel in Seoul, South Korea
p.2045

Characteristics of Combined Sewer Overflows in Nanjing and Selection of Intercepted Time
p.2056

The Evaluation of the Water Quality of Shenwo Reservoir Based on Fuzzy Comprehensive Evaluation Method
p.2061

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 594-597Research on Urban Water Demand Prediction

Research on Urban Water Demand Prediction

Article Preview

Abstract:

Water demand prediction adopts combined prediction method based on BP neural network prediction model, grey G (1,1) prediction model, time sequence prediction model (second multinomial exponential smoothing model) and single linear regression model (Cubics Ratio model). Empirical results show that combined prediction method makes comprehensive use of information of every separate prediction model, and thus enhances prediction accuracy.

You might also be interested in these eBooks

Advances in Industrial and Civil Engineering

Info:

Periodical:

Advanced Materials Research (Volumes 594-597)

Pages:

2037-2040

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.594-597.2037

Citation:

Cite this paper

Online since:

November 2012

Authors:

Kun Yang Wan

Keywords:

BP Neural Network (BPNN), Combined Prediction Method, Grey G (1,1) Model, Urban Water Resource

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] D. D. Daniel, P. A William: J. of environmental planning and management, Vol. 42(1999),p.707

[2] D. Agthe, R. Billings: Water resources research, Vol. 1980,16(3): 476-480.

[3] X. Mu, D. Whittington and J. Briscoe: Water resources research, Vol. 26(1990), p.521

[4] P. Latinopoulos, N. Mylopoulos and Y. Mylopoulos: Water Resources Management, 11(1977), p.263

DOI: 10.1023/a:1007968900540

[5] D. C. Reid, A. J. Lamb, and A. Lilly, et al: Water Policy, Vol. 3(2001), p.273

[6] K. D. Frederick: Climatic Change, Vol. 24(1993), p.83

[7] M. Ruth, C. Bernier and N. Jollands, et al: Water Resources Management, Vol. 21(2007), p.1031

[8] J. K. O'Hara, K. P. Georgakakos: Water Resources Management, Vol.22(2008), p.1477

[9] Y.Wang: Systems engineering, Vol. 14(1996),p.60 (In Chinese)

[10] H.N. Duan, S.H. Mo and B. Shen: Water Resources and Power, Vol. 28(2010),p.19 (In Chinese)

[11] S.W. Wang, et al: China Rural Water and Hydropower, 2(2010), p.29 (In Chinese)

[12] X.J. Xue, et al: J. of Soil and Water Conservation, Vol.16(2002), p.83 (In Chinese)

[13] X.M. Wei, S.Y. Zhang: J. of Hebei University of Technology, Vol.32(2003),p.75 (In Chinese)

[14] H. Fang, et al: Northwest Water Resources & Water Engineering, Vol.14(2003),p.1 (In Chinese)

[15] J.P. Liu, M.Q. Chang: J. of China Hydrology, Vol.27(2007),p.12 (In Chinese)

[16] L.X. Wang, et al: J. of Arid Land Resources and Environment, Vol.25(2011),p.106 (In Chinese)

[17] D.Z. Wang, et al: Advances in Water Science, Vol.13(2002), p.49 (In Chinese)

[18] W.F. Bao, J.S. Huang and H. Xie: Arid Land Geography, Vol.29(2006),p.29 (In Chinese)

[19] L. Zhang, et al: J. of Natural Resource, Vol.24(2009),p.328 (In Chinese)

[20] Y. He, L. Peng and S.P. Geng: J. of Water Resources& Water Engineering, Vol.22(2011),p.83 (In Chinese)