Title
Water Demand Analysis In Urban Region By Neural Network Models
Keywords
Neural network model; Optimal operation; Water demand forecasting; Water supply
Abstract
Statistical water demand models are usually developed as time series coefficients using historically available water demand data, together with any other relevant variables. But structure identification turns out difficult for most of the applications. This study would count on the artificial neural networks (ANN) to forecast the water demand patterns. The ANN model may exhibit a nonlinear feature learned from historical data, in the same way as humans learn from experience. The nonlinearity, high complexity, and uncertainty associated with water demands may favor the potential use of ANNs to compete with or outperform the conventional time series methods for forecasting the similar topics. If the ANNs model is learned correctly, as verified by the accuracy of the predictions using input data not used during the training, the ANN algorithm can be robust with low computation time requirements, even if there are some errors or noise in the input data. Two types of cities, including Oviedo (fast growth) and Winter Springs (slow growth) in the Great Orlando Metropolitan Area in Florida were investigated with respect to monthly data. Such pattern recognition practices would help water utilities identify the expansion and operation strategies in water distribution systems in the long run. Copyright ASCE 2006.
Publication Date
12-1-2007
Publication Title
8th Annual Water Distribution Systems Analysis Symposium 2006
Number of Pages
48-
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1061/40941(247)48
Copyright Status
Unknown
Socpus ID
40549145219 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/40549145219
STARS Citation
Chang, Ni Bin and Makkeasorn, Ammarin, "Water Demand Analysis In Urban Region By Neural Network Models" (2007). Scopus Export 2000s. 6223.
https://stars.library.ucf.edu/scopus2000/6223