Techniques for predicting total phosphorus in urban stormwater runoff at unmonitored catchments

Water Environment Research

Sivakumar²

2008

Self Cite

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. Water Environ. Res., 80, 4 (2008). IntroductionProgressive urbanization has led to the degradation of many waterways. To prevent this from further occurring, best management practices must be implemented. To do this effectively, techniques must be used to predict the extent of the water quality problem. This is often a difficult task when considering the wide range of interrelated processes that affect urban stormwater quality. As a result, empirical techniques have been developed to quantify the extent of the water pollution problem.Artificial neural network (ANN) models have been used in a number of previous studies to forecast environmental variables (Maier and Dandy, 2000). For example, Lek et al. (1996Lek et al. ( , 1999 used ANN to predict nutrient concentrations at approximately 1000 predominantly ''natural'' waterways located in the United States. It was observed that ANN models were more accurate than multiple linear regression models constructed on logarithmically transformed data. The main advantage of ANN models is their ability to model complex nonlinear phenomena, without specifying the exact functional forms associated with the system under study (Lek et al., 1999). This infers that ANN models may be applicable when modeling urban stormwater quality.

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Comparison of Artificial Neural Network and Regression Models in the Prediction of Urban Stormwater Quality

Water Environment Research

Sivakumar²

2008

Self Cite

“…The data set was initially preprocessed. A number of atypical catchments were removed from the data set using criteria specified in a previous study by May & Sivakumar (2003). Such catchments were deemed to lie outside the modelling domain.…”

Section: Methodsmentioning

confidence: 99%

“…Following data preprocessing, regression models were constructed. In accordance with a previous article by May & Sivakumar (2003), EMCs were used as the dependent variable, as opposed to event load. This was to negate the necessity of modelling the amount of known correlation between load and runoff volume.…”

Section: Methodsmentioning

confidence: 99%

Prediction of heavy metal concentrations in urban stormwater

Sivakumar

2009

Water & Environment J

Heavy metals in urban stormwater runoff can adversely impact aquatic ecosystems. Successful management of such systems requires the accurate prediction of contaminant concentrations. This has created the need for simplistic statistical models. In this study, models were constructed to predict three of the most prevalent heavy metal constituents in urban stormwater: copper (Cu), lead (Pb) and zinc (Zn). Data from the United States, obtained during the Nationwide Urban Runoff Program (NURP), were used to calibrate and verify the models. A comparison of the models revealed that regression models were more accurate than the landuse-based or metropolitan area averages of event mean concentration (EMC).

Prediction of Nutrient Concentrations in Urban Storm Water

Sivakumar

2009

J. Environ. Eng.

Excessive quantities of nutrients in urban storm-water runoff can lead to problems such as eutrophication in receiving water bodies. Accurate process based models are difficult to construct due to the vast array of complex phenomena affecting nutrient concentrations. Furthermore, it is often impossible to successfully apply process based models to catchments with limited or no sampling. This has created the need for simple models capable of predicting nutrient concentrations at unmonitored catchments. In this study, simple statistical models were constructed to predict six different types of nutrients present in urban storm-water runoff: ammonia ͑NH 3 ͒, nitrogen oxides ͑NO x ͒, total Kjeldahl nitrogen, total nitrogen, dissolved phosphorus, and total phosphorus. Models were constructed using data from the United States, collected as a part of the Nationwide Urban Stormwater Program more than two decades ago. Comparison between the models revealed that regression models were generally more applicable than the simple estimates of mean concentration from homogeneous subsets, separated based upon land use or the metropolitan area. Regression models were generally more accurate and provided valuable insight into the most important processes influencing nutrient concentrations in urban storm-water runoff.