Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.
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.
May
D
D
Sustainable Water and Energy Research Group, School of Civil, Mining, and Environmental Engineering, Faculty of Engineering, University of Wollongong, Australia.
Sivakumar
M
M
D007854:Lead / Q000032:analysis; D016571:Neural Networks, Computer; D010100:Oxygen / Q000737:chemistry; D010758:Phosphorus / Q000032:analysis; D011891:Rain; D012044:Regression Analysis; D014505:Urban Population; D014865:Waste Disposal, Fluid; D014867:Water / Q000737:chemistry* / Q000592:standards; D014872:Water Movements; D014876:Water Pollution / Q000517:prevention & control*
D014867:Water; D010758:Phosphorus; D007854:Lead; D010100:Oxygen