Abstract
Stormwater runoff is often contaminated by human activities. Stormwater discharge into water bodies significantly contributes to environmental pollution. The choice of suitable treatment technologies is dependent on the pollutant concentrations. Wastewater quality indicators such as biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total suspended solids (TSS), and total dissolved solids (TDS) give a measure of the main pollutants. The aim of this study is to provide an indirect methodology for the estimation of the main wastewater quality indicators, based on some characteristics of the drainage basin. The catchment is seen as a black box: the physical processes of accumulation, washing, and transport of pollutants are not mathematically described. Two models deriving from studies on artificial intelligence have been used in this research: Support Vector Regression (SVR) and Regression Trees (RT). Both the models showed robustness, reliability, and high generalization capability. However, with reference to coefficient of determination R2 and root‐mean square error, Support Vector Regression showed a better performance than Regression Tree in predicting TSS, TDS, and COD. As regards BOD5, the two models showed a comparable performance. Therefore, the considered machine learning algorithms may be useful for providing an estimation of the values to be considered for the sizing of the treatment units in absence of direct measures.
Highlights
In a rapidly developing world, water is more and more extensively polluted [1]
The aim of this study is to provide an indirect methodology for the estimation of the main wastewater quality indicators, based on some characteristics of the drainage basin
The catchment is seen as a black box: the physical processes of accumulation, washing, and transport of pollutants are not mathematically described.Two models deriving from studies on artificial intelligence have been used in this research: Support Vector Regression (SVR) and Regression Trees (RT)
Summary
In a rapidly developing world, water is more and more extensively polluted [1]. Urban areas can pollute water in many ways. Modifications of the urban environment lead to changes in the physical and chemical characteristics of urban stormwater [2]. The increase of impervious surfaces affects many components of the hydrologic cycle altering the pervious interface between the atmosphere and the soil. Runoff is heavily contaminated by pollution arising from industrial activities, civil emissions, and road traffic emissions. Stormwater collected in sewer systems and discharged into water bodies significantly contributes to environmental pollution
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