Water Resources Management

, Volume 15, Issue 5, pp 299–321 | Cite as

Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks

  • Ashu JainEmail author
  • Ashish Kumar Varshney
  • Umesh Chandra Joshi


The efficient operation and management of an existing water supply system require short-term water demand forecasts as inputs. Conventionally, regression and time series analysis have been employed in modelling short-term water demand forecasts. The relatively new technique of artificial neural networks has been proposed as an efficient tool for modelling and forecasting in recent years. The primary objective of this study is to investigate the relatively new technique of artificial neural networks for use in forecasting short-term water demand at the Indian Institute of Technology, Kanpur. Other techniques investigated in this study include regression and time series analysis for comparison purposes. The secondary objective of this study is to investigate the validity of the following two hypotheses: 1) the short-term water demand process at the Indian Institute of Technology, Kanpur campus is a dynamic process mainly driven by the maximum air temperature and interrupted by rainfall occurrences, and 2) occurrence of rainfall is a more significant variable than the rainfall amount itself in modelling the short-term water demand forecasts. The data employed in this study consist of weekly water demand at the Indian Institute of Technology, Kanpur campus, and total weekly rainfall and weekly average maximum air temperature from the City of Kanpur, India. Six different artificial neural network models, five regression models, and two time series models have been developed and compared. The artificial neural network models consistently outperformed the regression and time series models developed in this study. An average absolute error in forecasting of 2.41% was achieved from the best artificial neural network model, which also showed the best correlation between the modelled and targeted water demands. It has been found that the water demand at the Indian Institute of Technology, Kanpur campus is better correlated with the rainfall occurrence rather than the amount of rainfall itself.

artificial neural networks municipal water use modelling regression analysis short-term water demand forecasting time series analysis water resources management 


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  1. Anderson, R. C., Miller, T. A. and Washburn, M. C.: 1980, ‘Water Savings from Lawn Watering Restrictions During a Drought Year in Fort Collins, Colorado’, Water Resour. Bull., 16(4), 642–645.Google Scholar
  2. Beaudet, B. and Roberts, R. L.: 2000, ‘A Perspective on the Global Water Marketplace’, J. Amer. Water Works Assoc. 92(4), 10–12.Google Scholar
  3. Crommelynck, V., Duquesne, C., Mercier, M. and Miniussi, C.: 1992, ‘Daily and Hourly Water Consumption Forecasting Tools Using Neural Networks’, Proc. of the AWWA's Annual Computer Specialty Conference, Nashville, Tennessee, pp. 665–676.Google Scholar
  4. Franklin, S. L. and Maidment, D. R.: 1986, ‘An Evaluation of Weekly and Monthly Time Series Forecasts of Municipal Water Use’, Water Resour. Bull. 22(4), 611–621.Google Scholar
  5. Graeser Jr., H. J.: 1958, ‘Meter Records in Systems Planning’, J. Amer. Water Works Assoc. 50(11), 1395–1402.Google Scholar
  6. Howe, C. W. and Linaweaver, F. P.: 1967, ‘The Impact of Price on Residential Water Demand and Its Relation to Systems Design’, Water Resour. Res. 3(1), 13–22.Google Scholar
  7. Hughes, T. C.: 1980, ‘Peak Period Design Standards for Small Western U.S. Water Supply’, Water Resour. Bull. 16(4), 661–667.Google Scholar
  8. IMD: 1998, Weekly Weather Reports, India Meteorological Department, India, 1989–1998.Google Scholar
  9. IWD: 1998, Daily Water Supply Record, Institute Work Department, IIT Kanpur, India, 1989–1998.Google Scholar
  10. Jowitt, P. W. and Xu, C.: 1992, ‘Demand Forecasting for Water Distribution Systems’, Civil Engg. Sys. 9, 105–121.Google Scholar
  11. Leon, C., Martin, S., Jose, M. E. and Luque, J.: 2000, ‘EXPLORE-Hybrid Expert System for Water Networks Management’, J. Water Resour. Plng. and Mgmt. 126(2), 65–74.Google Scholar
  12. MWH: 1996, Manual on Water Supply and Treatment, Central Public Health and Environmental Engineering Organization, Ministry of Works and Housing, New Delhi, India.Google Scholar
  13. Maidment, D. R. and Parzen, E.: 1984a, ‘Monthly Water Use and its Relationship to Climatic Variables in Texas’, Water Resour. Bull. 19(8), 409–418.Google Scholar
  14. Maidment, D. R. and Parzen, E.: 1984b, ‘Time Patterns of Water Use in Six Texas Cities’, J. Water Resour. Plng and Mgmt. 110(1), 90–106.Google Scholar
  15. Maidment, D. R., Miaou, S. P. and Crawford, M. M.: 1985, ‘Transfer Function Models of Daily Urban Water Use’, Water Resour. Res. 21(4), 425–432.Google Scholar
  16. Miaou, S. P.: 1990, ‘A Class of Time Series Urban Water Demand Models with Non-Linear Climatic Effects’, Water Resour. Res. 26(2), 169–178.Google Scholar
  17. Oh, H. S. and Yamauchi, H.: 1974, ‘An Economic Analysis of the Patterns and Trends in Water Consumption within the Service Area of the Honolulu Board of Water Supply’, Report No. 84, Hawaii Water Resources Research Center, University of Honolulu, Honolulu, pp. 647–666.Google Scholar
  18. Rumelhart, D. E., Hinton, G. E. and Williams, R. J.: 1986, ‘Learning Internal Representations by Error Back Propagation’, in D. E. Rumelhart and J. L. McClelland (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, Foundations, The MIT Press, Ch 8.Google Scholar
  19. Smith, J. A.: 1988, ‘A Model of Daily Municipal Water Use for Short-Term Forecasting’, Water Resour. Res. 24(2), 201–206.Google Scholar
  20. Steiner, R. C. and Smith J. A.: 1983, Short-Term Municipal Water Use Forecasting, Paper Presented at the ASCE's National Specialty Conference, Tampa, Florida.Google Scholar
  21. Valdes, J. B. and Sastri, T.: 1989, ‘Rainfall Intervention Analysis for On-Line Applications’, J. Water Resour. Plng. and Mgmt. 115(4), 397–415.Google Scholar
  22. Weeks, C. R. and McMahon, T. A.: 1973, ‘A Comparison of Water Utilities in Australia and the U.S.’, J. American Water Works Assoc. 65(4), 232–241.Google Scholar
  23. Zurada, M. J.: 1992, An Introduction to Artificial Neural Systems, PWS Publishing Company, Mumbai, India.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Ashu Jain
    • 1
    Email author
  • Ashish Kumar Varshney
    • 1
  • Umesh Chandra Joshi
    • 1
  1. 1.Department of Civil EngineeringIndian Institute of TechnologyKanpurIndia

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