Machine Learning–Based Source Identification in Sewer Networks
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 8
Abstract
Motivated by the valuable epidemiological information it reveals, wastewater surveillance has received significant attention in recent years. Furthermore, monitoring the water quality in sewer systems has been shown to provide useful information to support wastewater treatment operations. Yet, a critical need still exists for developing novel approaches for rapid and efficient source identification of chemical and biological species of interest in sewer systems. A limited number of source identification approaches have been proposed in previous literature, and the majority of these approaches employed various simplifying assumptions that limit their usage in real-life applications. In this study, a machine learning–based simulation-optimization framework was developed to determine the characteristics (i.e., concentration and loading pattern) of multiple simultaneous injection sources in sewer systems. The simulation was conducted using a surrogate model in the form of a multilayer perceptron neural network, which was trained using simulation results derived from the Storm Water Management Model (SWMM). The simulation model was then coupled with a genetic algorithm to reveal the characteristics of multiple sources that reproduce the concentration patterns observed at one or more monitoring locations in the sewer system. The proposed framework was applied to a range of injection scenarios and was able to identify the characteristics of multiple simultaneous injection sources under different conditions. The results showed that the residence time plays a significant role in the identifiability of the injection source location. The proposed framework is applicable to a wide number of source identification applications, including contamination source identification and wastewater-based epidemiology.
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Data Availability Statement
All data, models, and codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by funding from the University of Illinois Chicago, and the National Science Foundation under Grant No. 2015603.
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Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 8 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 15, 2022
Accepted: Mar 14, 2023
Published online: May 27, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 27, 2023
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