Document Detail

Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.
MedLine Citation:
PMID:  18254392     Owner:  NLM     Status:  MEDLINE    
Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.
D May; M Sivakumar
Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Water environment research : a research publication of the Water Environment Federation     Volume:  80     ISSN:  1061-4303     ISO Abbreviation:  Water Environ. Res.     Publication Date:  2008 Jan 
Date Detail:
Created Date:  2008-02-07     Completed Date:  2008-02-27     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886167     Medline TA:  Water Environ Res     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4-9     Citation Subset:  IM    
Sustainable Water and Energy Research Group, School of Civil, Mining, and Environmental Engineering, Faculty of Engineering, University of Wollongong, Australia.
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MeSH Terms
Lead / analysis
Neural Networks (Computer)*
Oxygen / chemistry
Phosphorus / analysis
Regression Analysis
Urban Population
Waste Disposal, Fluid
Water / chemistry*,  standards
Water Movements
Water Pollution / prevention & control*
Reg. No./Substance:
7439-92-1/Lead; 7723-14-0/Phosphorus; 7732-18-5/Water; 7782-44-7/Oxygen

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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