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Article

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

1
Institute of Telecommunications, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
2
Blue Technologies sp. z o.o., ul. Puławska 266/221, 02-684 Warsaw, Poland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Vassilis Plagianakos, Sotiris Tasoulis and Matjaž Finšgar
Sensors 2021, 21(10), 3426; https://doi.org/10.3390/s21103426
Received: 19 March 2021 / Revised: 10 May 2021 / Accepted: 11 May 2021 / Published: 14 May 2021
(This article belongs to the Special Issue Machine Learning Applied to Sensor Data Analysis)
Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m. View Full-Text
Keywords: random forest; XGBoost; water pollution; machine learning; sensors random forest; XGBoost; water pollution; machine learning; sensors
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MDPI and ACS Style

Buras, M.P.; Solano Donado, F. Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network. Sensors 2021, 21, 3426. https://doi.org/10.3390/s21103426

AMA Style

Buras MP, Solano Donado F. Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network. Sensors. 2021; 21(10):3426. https://doi.org/10.3390/s21103426

Chicago/Turabian Style

Buras, Magdalena Paulina, and Fernando Solano Donado. 2021. "Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network" Sensors 21, no. 10: 3426. https://doi.org/10.3390/s21103426

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