Short-term water demand forecasting using Artificial Neural Networks: IIT Kanpur experience

被引：0
|
作者：
Jain, A [1 ]
Joshi, UC [1 ]
Varshney, AK [1 ]
机构：
[1] Indian Inst Technol, Dept Civil Engn, Kanpur 208016, Uttar Pradesh, India
来源：
15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS | 2000年
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 a 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.
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页码：459 / 462
页数：4
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