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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
Article

Abstract

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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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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