Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods

E3S Web of Conferences. 2017;22:00174 DOI 10.1051/e3sconf/20172200174


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Journal Title: E3S Web of Conferences

ISSN: 2267-1242 (Online)

Publisher: EDP Sciences

LCC Subject Category: Geography. Anthropology. Recreation: Environmental sciences

Country of publisher: France

Language of fulltext: French, English

Full-text formats available: PDF



Szeląg Bartosz
Barbusiński Krzysztof
Studziński Jan
Bartkiewicz Lidia


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Time From Submission to Publication: 6 weeks


Abstract | Full Text

In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance.