Hybrid Fuzzy Regression–Artificial Neural Network for Improvement of Short-Term Water Consumption Estimation and Forecasting in Uncertain and Complex Environments: Case of a Large Metropolitan City
Publication: Journal of Water Resources Planning and Management
Volume 138, Issue 1
Abstract
This study presents a hybrid approach consisting of artificial neural network (ANN), fuzzy linear regression (FLR), and analysis of variance (ANOVA) for improvement of water consumption forecasting. Hence, this approach can be easily applied to uncertain or certain, or complex environments given its flexibility. The proposed hybrid approach is applied to forecast short-term water consumption in Tehran, Iran from April 5, 2004, to March 21, 2009. In this study, daily water consumption is viewed as the resultant of future and historical meteorological data. Implementation of the hybrid approach in a large metropolitan city such as Tehran seems to be ideal because of potential nonlinearity and uncertainty in the water consumption function of Tehran, Iran. The results of mean absolute percentage error (MAPE) indicate that selected ANN outperforms selected FLR on warm days. However, both ANN and FLR are ideal for cold days. To verify and validate the results, a sensitivity analysis is carried out by changing the train and test data sets. Finally, the comparison of the MAPE results of the hybrid approach with conventional linear regression confirms its considerable superiority for both warm and cold days.
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Acknowledgments
The writers are grateful for the valuable comments and suggestions from the respected reviewers. Their valuable comments and suggestions have enhanced the strength and significance of our paper. The writers would like to acknowledge the financial support of University of Tehran for this research under Grant No. UNSPECIFIED8106013/1/07.
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Published In
Journal of Water Resources Planning and Management
Volume 138 • Issue 1 • January 2012
Pages: 71 - 75
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Apr 13, 2010
Accepted: Mar 10, 2011
Published online: Mar 12, 2011
Published in print: Jan 1, 2012
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