Ai System for Real Time Monitoring of Water Quality

Abstract

Monitoring water quality is critical for mitigating risks to human health and the environment. It is also essential for ensuring the high quality of water-based and water-dependent products and services. Conventional monitoring often involves the collection of water samples in the field and subsequent analysis in the laboratory. This is costly and introduces a large time delay between a potential contamination incident and a possible reactive measure. Here, we developed a real-time monitoring system based on Artificial Intelligence (AI) for field deployable sensors. We used data obtained from full-scan UV-vis and fluorescence sensors as a proof of concept. This multi-sensor system consists of (a) anomaly detection that uses multivariate statistical methods to detect any anomalous state in an aqueous environment and (b) anomaly identification, using Machine Learning (ML) to classify the anomaly into one of a-prior known categories. For a proof of concept, we tested this methodology on a supply of municipal drinking water and a few representative contaminants applied in a laboratory-controlled environment. The outcomes confirm the ability for the multi-sensor system to detect and identify changes in water quality due to incidences of contamination. The method may be applied to numerous other areas where water quality should be measured online and in real time, such as in surface-water, urban runoff, or food and industrial process water.

Keywords: Real Time, Monitoring, Water Quality, AI system