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Detection of untreated sewage discharges to watercourses using machine learning

Environmental Studies and Forestry

Detection of untreated sewage discharges to watercourses using machine learning

P. Hammond, M. Suttie, et al.

This research, conducted by Peter Hammond, Michael Suttie, Vaughan T. Lewis, Ashley P. Smith, and Andrew C. Singer, introduces innovative machine learning techniques to identify untreated sewage spills from wastewater treatment plants with over 96% accuracy. The findings reveal significant potential non-compliance in effluent discharges, providing valuable insights for water management and regulatory oversight.

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Playback language: English
Abstract
This study presents novel methodologies for identifying untreated sewage spills from wastewater treatment plants (WWTPs) using machine learning (ML). Daily effluent flow patterns from two WWTPs, supplemented by operator-reported incidents, served as training data. The ML model achieved over 96% accuracy in classifying spill and no-spill events. Retrospective analysis identified 926 days without reported spills that were classified as potential spills, suggesting non-compliant discharges at both WWTPs. This ML approach can assist water companies and regulatory agencies in improving WWTP management and regulatory oversight.
Publisher
npj Clean Water
Published On
Mar 11, 2021
Authors
Peter Hammond, Michael Suttie, Vaughan T. Lewis, Ashley P. Smith, Andrew C. Singer
Tags
machine learning
wastewater treatment
sewage spills
effluent discharge
regulatory oversight
water management

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