15th International Conference on Pattern Recognition (ICPR'00) - Volume 2 Short-Term Water Demand Forecasting Using Artificial Neural Networks: IIT Kanpur Experience Barcelona, Spain September 03-September 08 ISBN: 0-7695-0750-6
In this paper, the relatively new technique of Artificial Neural Networks (ANNs) has been investigated for use in forecasting short-term water demand. Other methods investigated for comparison purposes include regression and time series analysis. The data employed in this study consist of weekly water demand at the Indian Institute of Technology (IIT) Kanpur campus, and rainfall and maximum temperature from the City of Kanpur, India. The ANN models consistently outperformed the regression and time series models developed in this study. An average error in forecasting of 3.28 % was achieved from the best ANN model. It has been found that the water demand at IIT Kanpur is better correlated with the rainfall occurrence rather than the amount of rainfall.
Citation:
Ashu Jain, Umesh Chandra Joshi, Ashish Kumar Varshney, "Short-Term Water Demand Forecasting Using Artificial Neural Networks: IIT Kanpur Experience," icpr, vol. 2, pp.2459, 15th International Conference on Pattern Recognition (ICPR'00) - Volume 2, 2000 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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