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Comparison of Artificial Neural Network and Regression Models in the Prediction of Urban Stormwater Quality
Author(s) -
May D.,
Sivakumar M.
Publication year - 2008
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/106143007x184591
Subject(s) - stormwater , artificial neural network , kjeldahl method , linear regression , environmental science , regression analysis , total suspended solids , water quality , regression , empirical modelling , predictive modelling , environmental engineering , statistics , hydrology (agriculture) , chemical oxygen demand , computer science , machine learning , engineering , mathematics , surface runoff , nitrogen , ecology , chemistry , simulation , organic chemistry , geotechnical engineering , wastewater , biology
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.