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Water 2017, 9(2), 105; doi:10.3390/w9020105

Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators

1
Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, via G. Di Biasio 43, 03043 Cassino, Italy
2
Department of Civil, Architectural and Environmental Engineering, University of Napoli “Federico II”, via Claudio 21, 80125 Napoli, Italy
*
Author to whom correspondence should be addressed.
Received: 21 November 2016 / Accepted: 6 February 2017 / Published: 9 February 2017
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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. View Full-Text
Keywords: water quality; machine learning; Support Vector Regression; Regression Tree; wastewater;  treatment units water quality; machine learning; Support Vector Regression; Regression Tree; wastewater;  treatment units
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Granata, F.; Papirio, S.; Esposito, G.; Gargano, R.; de Marinis, G. Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators. Water 2017, 9, 105.

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