P2O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants
Publication: Journal of Environmental Engineering
Volume 149, Issue 9
Abstract
Wastewater treatment plants (WWTPs) are critical infrastructures responsible for processing wastewater before discharging effluent to rivers and other potential uses. WWTPs use large, connected deep tunnels for storing sanitary and wet-weather flows for treatment. However, wastewater in those systems cannot exceed safe tunnel levels in order to prevent overflows of untreated wastewater into the environment. Further, WWTPs are among the 16 national lifeline infrastructure sectors in which the utilization of sensor technology has increased, making the sectors vulnerable to all forms of cyber threats. Considering these challenges, the work presented in this manuscript uncovers the role of AI at WWTPs by focusing on two problems: tunnel water-level prediction and detection of security threats. This is done by proposing an AI framework: (prediction, protection, and optimization). The prediction module forecasts the tunnel water level using deep-learning models based on the current wastewater flow in the tunnel and other inputs from the sensors and gauges. The protection module focuses on classifying the intentionality of an anomaly, i.e., whether an attack is adversarial in nature or merely an outlier, using recurrent neural network models. Last, the optimization module aims to provide actionable recommendations to pump operators using a genetic algorithm. The experimental results of indicate that the prediction module can predict the tunnel water level with 85% accuracy, and the protection module can detect about 97% of intentional attacks on WWTPs. AI models within are evaluated; the experimental results are presented and discussed.
Practical Applications
This manuscript presents , which is a novel AI framework that can predict about 85% of wastewater overflow incidences and about 95% of intentional cyberattacks on a WWTP, as indicated in the experiments. The deployment of at a WWTP is essential, especially considering the adverse effects of overflowing wastewater on the environment (i.e., rivers and other water bodies). Moreover, cyberattacks on WWTPs can be subtle, making them challenging to detect; on average, most of them are noticed within one week to one month after the attack. This makes national infrastructure vulnerable to external and internal threats, influencing the well-being of water bodies and overall national security. provides a real-time monitoring interface and can recommend optimal actions in different scenarios (i.e., outliers) for pump operators and process engineers at WWTPs.
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Data Availability Statement
The code for will be made available based on requests to the corresponding author. The SWaT (Goh et al. 2016) (https://itrust.sutd.edu.sg/itrust-labs_datasets/) and SMOD (Laso et al. 2017) are open-source data sets that can be obtained by contacting the original owners of the data. The third data set cannot be shared due to a nondisclosure agreement with the WWTP.
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Journal of Environmental Engineering
Volume 149 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 29, 2022
Accepted: Mar 21, 2023
Published online: Jun 17, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 17, 2023
ASCE Technical Topics:
- Engineering fundamentals
- Environmental engineering
- Geotechnical engineering
- Infrastructure
- Infrastructure vulnerability
- Lifeline systems
- Measurement (by type)
- Sensors and sensing
- Tunnels
- Wastewater management
- Wastewater treatment plants
- Water and water resources
- Water level
- Water management
- Water supply
- Water treatment
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