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 Resources Research

Volumn 48, Issue 1, 2012, Pages

  Adamowski, Jan ^a   Fung Chan, Hiu ^a   Prasher, Shiv O ^a   Ozga Zielinski, Bogdan ^b   Sliusarieva, Anna ^a  

^a MCGILL UNIVERSITY   (Canada)

^b INSTITUTE OF METEOROLOGY AND WATER MANAGEMENT   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; COEFFICIENT OF DETERMINATION; DAILY WATER DEMAND; EFFICIENCY INDEX; KEY VARIABLES; LEAD TIME; MAXIMUM TEMPERATURE; MONTREAL , CANADA; MULTIPLE LINEAR REGRESSIONS; MULTIPLE NONLINEAR REGRESSION; NETWORK MODELS; NON-LINEAR REGRESSION; RELATIVE PERFORMANCE; ROOT MEAN SQUARE ERRORS; SUMMER MONTHS; SUSTAINABLE MANAGEMENT; TOTAL PRECIPITATION; URBAN WATER SUPPLY SYSTEM; URBAN WATERS;

DISCRETE WAVELET TRANSFORMS; FORECASTING; LINEAR REGRESSION; MEAN SQUARE ERROR; WATER CONSERVATION; WATER SUPPLY; WATER SUPPLY SYSTEMS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; COST-BENEFIT ANALYSIS; DIURNAL VARIATION; LINEARITY; NONLINEARITY; OPTIMIZATION; PRECIPITATION (CLIMATOLOGY); SUSTAINABLE DEVELOPMENT; WATER DEMAND; WATER SUPPLY; WAVELET ANALYSIS;

CANADA; MONTREAL; QUEBEC [CANADA];

EID: 84856246099     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2010WR009945     Document Type: Article

References (71)

1. Adamowski, J. (2007), Development of a short-term river flood forecasting method based on wavelet analysis, J. Hydrol., 353(3-4), 247-266, doi:10.1016/j.jhydrol.2008.02.013.
2. Adamowski, J. (2008a), Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis, J. Hydrol., 353, 247-266, doi:10.1016/j.jhydrol.2008.02.013.
3. Adamowski, J. (2008b), Peak daily water demand forecast modeling using artificial neural networks, J. Water Resour. Plan. Manage., 134(2), 119-128, doi:10.1061/(ASCE)0733-9496(2008)134:2(19). (Pubitemid 351279617)
4. Adamowski, J. (2008c), River flow forecasting using wavelet and cross-wavelet transform models, J. Hydrol. Processes, 22, 4877-4891, doi:10.1002/hyp.7107.
5. Adamowski, J., and H. F. Chan (2011), A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 407, 28-40, doi:10.1016/j.jhydrol.2011.06.013.
6. Adamowski, J., and C. Karapataki (2008), Comparison of multivariate regression and artificial neural networks for peak urban water demand forecasting : the evaluation of different ANN learning algorithms, J. Hydrol. Eng., 15(10), 729-743, doi:10.1061/(ASCE)HE.1943-5584.0000245.
7. Adamowski, J., and K. Sun (2010), Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds, J. Hydrol., 390, 85-90, doi:10.1016/j.jhydrol.2010.06. 033.
8. Anderson, R., T. Miller, and M. Washburn (1980), Water savings from lawn watering restrictions during a drought year in Fort Collins, Colorado, Water Resour. Bull., 16(4), 642-645, doi:10.1111/j.1752-1688. 1980.tb02443.
9. Asefa, T., M. Kemblowski, M. McKee, and A. Khalil (2006), Multi-time scale stream flow predictions: The support vector machines approach, J. Hydrol., 318(1-4), 7-16, doi:10.1016/j.jhydrol.2005.06.001. (Pubitemid 43120784)
10. Aubertin, L., A. Aubin, G. Pelletier, D. Curodeau, M. Osseyrane, and P. Lavallee (2002), Identifying and Prioritizing Infrastructure Rehabilitation, North American Society for Trenchless Technology, Liverpool, NY.
11. Banerjee, P., R. K. Prasad, and V. S. Singh (2009), Forecasting of ground-water level in hard rock region using artificial neural network, Environ. Geol., 58(6), 1239-1246, doi:10.1007/s00254-008-1619-z.
12. Box, G. E. P., and G. Jenkins (1976), Time Series Analysis: Forecasting and Control, 2nd ed., San Francisco: Holden-Day.
13. Cannas, B., A. Fanni, L. See, and G. Sias (2006), Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning, Phys. Chem. Earth, 31, 1164-1171, doi:10.1016/j.oce.2006.03.020. (Pubitemid 44817630)
14. Cassuto, A. E., and S. Ryan (1979), Effect of price on the residential demand for water within an agency, Water Resour. Bull., 15(2), 345-353, doi:10.1111/j.1752-1688.1979.tb00337. (Pubitemid 9441567)
15. Cimen, M., and O. Kisi (2009), Comparison of two different data driven techniques in modeling surface water level fluctuations of Lake Van, Turkey, J. Hydrol., 378(3-4), 253-262, doi:10.1016/j.jhydrol.2009.09.029.
16. City of Montreal (2010), The Montreal Community Sustainable Development Plan 2010-2015, Montreal, QC.
17. Cogger, K. O. (2010), Nonlinear regression methods: A survey and extensions, Intell. Syst. Acc. Fin. Manage. 17, 19-39, doi:10.1002/isaf.311.
18. Dziegielewski, B., and D. Baumann (1992), Tapping alternatives: The benefits of managing urban water demands, Environment, 34(9), 6-11, 35-41.
19. Emiroglu, M., O. Bilhan, and O. Kisi (2011), Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel, Expert Syst. Appl., 38(1), 867-874, doi:10.1016/j.eswa.2010. 07.058.
20. Environment Canada (2010), Environmental Trends, CESI, 1(3).
21. Ghiassi, M., D. K. Zimbra, and H. Saidane (2008), Urban water demand forecasting with a dynamic artificial neural network model, J. Water Resour. Plan. Manage., 134(2), 138-146, doi10.1061/(ASCE)0733-9496(2008)134:2(138). (Pubitemid 351279619)
22. Hagan, M. T., and M. Menhaj (1994), Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Networks, 5(6), 989-993, doi:10.1109/72.329697.
23. Han, D., L. Chan, and N. Zhu (2007), Flood forecasting using support vector machines, Journal of Hydroinformatics, 9(4), 267-276, doi:10.2166/hydro.2007.027.
24. Haykin, S. (1998), Neural Networks-A Comprehensive Foundation, Prentice-Hall, Upper Saddle River, NJ.
25. Hughes, T. (1980), Peak period design standards for small western U.S. water supply, Water Resour. Bull., 16(4), 661-667, doi:10.1111/j.1752-1688.1980. tb02446.
26. Ivakhnenko, A. G. (1970), Heuristic self-organization in problems of engineering cybernetics, Automatica, 6(2), 207-219, doi:10.1016/0005-1098(70) 90092-0.
27. Jain, A., and L. Ormsbee (2002), Short-term water demand forecast modeling techniques: Conventional methods versus AI, J. Am. Water Works Assoc., 94(7), 64-72. (Pubitemid 34799823)
28. Karul, C., S. Soyupak, A. F. Cilesiz, N. Akbay, and E. Germen (2000), Case studies on the use of neural networks in eutrophication modeling, Ecol. Model., 134(2-3), 145-152, doi:10.1016/S0304-3800(00)00360-4.
29. Khan, M. S., and P. Coulibaly (2006), Application of support vector machine in lake water level prediction, J. Hydrol. Eng., 11(3), 199-205, doi:10.1061/(ASCE)1084-0699(2006)11:3(199).
30. Kim, T. W., and J. B. Valdes (2003), Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks, J. Hydrol. Eng., 8(6), 319-328, doi:10.1061/(ASCE)1084-0699(2003)8: 6(319). (Pubitemid 37436445)
31. Kisi, O. (2004), River flow modeling using artificial neural networks, J. Hydrol. Eng., 9(1), 60-63, doi:10.1061/(ASCE)1084-0699(2004)9:1(60). (Pubitemid 38122771)
32. Kisi, O. (2008), Stream flow forecasting using neuro-wavelet technique, Hydrol. Processes, 22, 4142-4152, doi:10.1002/hyp.7014.
33. Kisi, O. (2009), Neural networks and wavelet conjunction model for intermittent streamflow forecasting, J. Hydrol. Eng., 14(8), 773-782, doi:10.1061/(ASCE)HE.1943-5584.0000053.
34. Kisi, O. and M. Cimen (2011), A wavelet-support vector machine conjunction model for monthly streamflow forecasting, J. Hydrol., 399(1-2), 132-140, doi:10.1016/j.jhydrol.2010.12.041.
35. Lu, W., W. Wang, A. Y. T. Leung, S. Lo, R. K. K. Yuen, Z. Xuand H. Fan (2002), Air pollutant parameter forecasting using support vector machines, Neural Networks Proceedings of the 2002 International Joint Conference, 1, 630-635, doi:10.1109/IJCNN.2002.1005545. (Pubitemid 34657014)
36. Maas, T. (2003), What the experts think: Understanding urban water demand management in Canada. Retrieved from http://www.polispro-ject.org/publications/ byauthor/Maas,%20Tony.
37. Maidment, D., and S. Miaou (1986), Daily water use in nine cities, Water Resour. Res., 22(6), 845-851, doi:10.1029/WR022i006p00845. (Pubitemid 16586899)
38. Maidment, D., and E. Parzen (1984), Monthly water use and its relationship to climatic variables in Texas, Water Resour. Bull., 19(8), 409-418.
39. Maidment, D., S. Miaou, and M. Crawford (1985), Transfer function models of daily urban water use, Water Resour. Res., 21(4), 425-432, doi:10.1029/WR021i004p00425.
40. Maier, H., A. Jain, G. C. Dandy, and K. P. Sudheer (2010), Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions, Environ. Model. Software, 25, 891-909.
41. Maity, R., P. P. Bhagwat, and A. Bhatnagar (2010), Potential of support vector regression for prediction of monthly streamflow using endogenous property, Hydrol. Processes, 24(7), 917-923, doi:10.1002/hyp.7535.
42. Mallat S. (1999), A Wavelet Tour of Signal Processing, 2nd ed. Academic, New York.
43. Miaou, S. (1990), A class of time series urban water demand models with non-linear climatic effects, Water Resour. Res., 26, 169-178.
44. Minu, K. K., M. C. Lineesh, and C. Jessy John (2010), Wavelet neural networks for nonlinear time series analysis, Appl. Math. Sci., 4(50), 2485-2495.
45. Miyagishi, K., M. Ohsako, and H. Ichihashi (1999), Temperature prediction from regional spectral model by neurofuzzy GMDH, Proc. of The Second Asia-Pacific Conference on Industrial Engineering and Management Systems, Kanazawa, Japan, 705-708.
46. Msiza, I. S., F. V. Nelwamondo, and T. Marwala (2008), Water demand prediction using artificial neural networks and support vector regression, J. Comput., 3(11), 1-8, doi:10.4304/jcp.3.11.1-8.
47. Ozbek, F. S., and H. Fidan (2009), Estimation of pesticides usage in the agricultural sector in Turkey using artificial neural network (ANN), J. Animal Plant Sci., 4(3), 373-378.
48. Partal, T. (2009), River flow forecasting using different artificial neural network algorithms and wavelet transform, Can. J. Civil Eng., 36(1), 26-38, doi:10.1139/L08-090.
49. Partal, T., and O. Kisi (2007), Wavelet and neuro-fuzzy conjunction model for precipitation forecasting, J. Hydrol., 342(1-2), 199-212, doi:10.1016/j. jhydrol.2007.05.026. (Pubitemid 47069364)
50. Pedhazur, E. J. (1982), Multiple Regression in Behavioral Research: Explanation and Prediction, Holt, Rinehart and Winston, New York.
51. Pramanik, N., and R. K. Panda (2009), Application of neural network and adaptive neuro fuzzy inference systems for stream flow prediction, Hydrol. Sci. J., 54(2), 247-260, doi:10.1623/hysj.54.2.247.
52. Rajasekaran, S., S. Gayathri, and T.-L. Lee (2008), Support vector regression methodology for storm surge predictions, Ocean Eng., 35, 1578-1587, doi:10.1016/j.oceaneng.2008.08.004.
53. Rubin, D. (2005), Down the drain: Dealing with Montreal's water wastage, McGill Reporter, 37(17), Montreal, QC.
54. Sahoo, G. B., and C. Ray (2006), Flow forecasting for a Hawaii stream using rating curves and neural networks, J. Hydrol., 317, 63-80, doi:10.1016/j.jhydrol.2005.05.008. (Pubitemid 41771018)
55. Sarycheva, L. (2003), Using GMDH in ecological and socio-economical monitoring problems, Syst. Anal. Model. Simul., 43(10), 1409-1414, doi:10.1080/02329290290024925. (Pubitemid 44112267)
56. Sethi, R. R., A. Kumar, S. P. Sharma, and H. C. Verma (2010), Prediction of water table depth in a hard rock basin by using artificial neural network, Int. J. Water Resour. Environ. Eng., 2(4), 95-102.
57. Shiri, J., and O. Kisi (2010), Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model, J. Hydrol., 394(3-4), 486-493.
58. Smith, J. (1988), A model of daily municipal water use for short-term forecasting, Water Resour. Res., 24(2), 201-206, doi:10.1029/WR024i002p 00201. (Pubitemid 18564923)
59. Solomatine, D., and A. Ostfeld (2008), Data driven modeling: Some past experiences and new approaches, J. Hydroinf., 10(1), 3-22.
60. Spider Financial (2011), NumXL Microsoft Excel add-in, Spider Financial, Chicago.
61. Sreekanth, P. D., N. Geethanjali, P. D. Sreedevi, S. Ahmed, N. R. Kumar, and P. D. K. Jayanthi (2009), Forecasting groundwater level using artificial neural networks, Curr. Sci., 96(7), 933-939.
62. Sudheer, K. P., P. C. Nayak, and K. S. Ramasastri (2003), Improving peak flow estimates in artificial neural network river flow models, Hydrol. Processes, 17(3), 677-686, doi:10.1002/hyp.5103. (Pubitemid 36257679)
63. Tiwari, M. K., and C. Chatterjee (2010a), A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting, J. Hydroinf., 13(3), 500-519, doi:10.2166/hydro.2010.142.
64. Tiwari, M. K., and C. Chatterjee (2010b), Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach, J. Hydrol., 394(3-4), 458-470, doi: 10.1016/j.jhydrol.2010.10. 001.
65. Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer, New York.
66. Wang, W., and J. Ding (2003), Wavelet network model and its application to the prediction of hydrology, Nat. Sci., 1(1), 67-71.
67. Wang, W. C., K. W. Chau, C. T. Cheng, and L. Qiu (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, J. Hydrol. 374(3-4), 294-306.
68. Wong, J. S., Q. Zhang, and Y. D. Chen (2010), Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast, Water Resour. Res., 46, W03506, doi:10.1029/2009WR008147.
69. XLSTAT (2011), XLSTAT Microsoft Excel add-in, Addinsoft SARL, Paris.
70. Yu, P.-S., S.-T. Chen, and I.-F. Chang (2006), Support vector regression for real-time flood stage forecasting, J. Hydrol., 328(3-4), 704-716, doi:10.1016/j.jhydrol.2006.01.021. (Pubitemid 44188765)
71. Zhou, S., T. McMahon, A. Walton, and J. Lewis (2000), Forecasting daily urban water demand: A case study of Melbourne, J. Hydrol., 236(3), 153-164, doi:10.1016/S0022-1694(00)00287-0.