Hybrid Box-Jenkins and neural network forecasting of potable water demand

Intelligent Engineering Systems Through Artificial Neural Networks

Volumn 6, Issue , 1996, Pages 801-807

  Wang, Qin ^a   Heller, Miriam ^b  

^a Bechtel Corp Inc   (United States)

^b UNIVERSITY OF HOUSTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; FORECASTING; MATHEMATICAL MODELS; NEURAL NETWORKS; POTABLE WATER; TIME SERIES ANALYSIS;

SEASONAL VARIATIONS;

WATER SUPPLY;

EID: 0030308786     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (10)

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2. Chen, S. (1991). Weather Sensitive Short-Term Load Forecasting Using Non-Fully Connected Artificial Neural networks, Ph. D. Thesis, University of Wisconsin-Milwaukee.
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7. Rumelhart, D. E., McClelland, J. L., and The PDP Research Group (1986). Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Vol. 1, MIT Press: Cambridge, MA.
8. Smith, J.A. (1988). A Model of Daily Municipal Water Use for Short-Term Forecasting. Water Resources Research. 24:210-206.
9. Thind, H. S. (1994). Forecasting Nonlinear Time Series Using Artificial Neural Networks: An Application to Daily Municipal Water Demand Forecasting, Master's Thesis. Department of Industrial Engineering, University of Houston.
10. Weígend, A. S. And Gershenfeld, N.A. (Eds.). (1993). Time Series Prediction: Forecasting the Future and Undrstanding the Past, Addison-Wesley: Redwood, CA.