Towards Water Systems Security and Sustainability Using Deep Learning

Abstract

Wastewater treatment plants (WWTPs) face significant challenges due to varying influent conditions, multiple operational constraints, and a constant lack of reliable datasets to manage and monitor water quality and flow using automated approaches. This paper introduces a novel framework showcasing soft sensors that are aimed at enhancing the sustainability and security of wastewater quality indicators using deep learning. We develop a trustworthy soft sensor that utilizes artificial intelligence (AI) approaches to provide nitrate NO3 predictions at the WWTP, as well as context-based evaluations to estimate overall predictive uncertainty. Contextual elements are injected into the model to allow for more accurate and relevant water quality monitoring, especially in different conditions (such as rain and snow). In addition, in this paper, we present a time-series Generative Adversarial Network (GAN), namely H2OGAN to address data scarcity and to improve model training by generating synthetic data that mirrors the statistical properties of water datasets from both controlled and real-world environments. Data in turn also train against data poisoning attacks on water supply systems, rendering these systems more secure. Our results indicate the potential uses of the integration of soft sensors and H2OGAN to significantly improve the operational efficiency of WWTPs by providing robust AI-driven tools for offering secure and sustainable water monitoring solutions.