Artificial intelligence-based monitoring system of water quality parameters for early detection of non-specific bio-contamination in water distribution systems

Silvia Tinelli, Ilan Juran

Research output: Contribution to journalArticle

Abstract

This research aims to simulate bio-contamination risk propagation under real-life conditions in the water distribution system (WDS) of Lille University’s Scientific City Campus (France), solving the source identification and the response modeling. Neglecting dynamic reactions and not considering the possible chemical decay of most of the contaminants leads to an overestimation of the exposed population. Therefore, unlike the available event detection models, this study considers the interrelated change of several water-quality parameters such as free chlorine concentration, pH, alkalinity, and total organic carbon (TOC) resulting from the pollutants blending. In fact, starting from regular WDS monitoring, the baseline thresholds for each of the mentioned parameters are established; then, significant deviations from the baseline are used as indication for contaminations. For this reason, the purpose of the research was to develop and demonstrate the feasibility of an artificial intelligence (AI)-based smart monitoring system that will effectively enable water operators to ensure a quasi real-time quality control for early chemical and/or bio-contamination detection and preemptive risk management. Advanced pattern recognizers, such as Support Vector Machines (SVMs), and innovative sensing technology solutions, such as Artificial Neural Network (ANN), have been used for this purpose, identifying the anomalies and the severity-level assessment.

Original languageEnglish (US)
Pages (from-to)1785-1792
Number of pages8
JournalWater Science and Technology: Water Supply
Volume19
Issue number6
DOIs
StatePublished - Jan 1 2019

Fingerprint

artificial intelligence
monitoring system
water quality
pollutant
quality control
artificial neural network
alkalinity
total organic carbon
chlorine
anomaly
monitoring
modeling
detection
contamination
water distribution system
parameter
water
chemical

Keywords

  • ANN
  • Bio-contaminations detection
  • EPANET-SMX
  • Pattern recognition
  • SVM

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

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abstract = "This research aims to simulate bio-contamination risk propagation under real-life conditions in the water distribution system (WDS) of Lille University’s Scientific City Campus (France), solving the source identification and the response modeling. Neglecting dynamic reactions and not considering the possible chemical decay of most of the contaminants leads to an overestimation of the exposed population. Therefore, unlike the available event detection models, this study considers the interrelated change of several water-quality parameters such as free chlorine concentration, pH, alkalinity, and total organic carbon (TOC) resulting from the pollutants blending. In fact, starting from regular WDS monitoring, the baseline thresholds for each of the mentioned parameters are established; then, significant deviations from the baseline are used as indication for contaminations. For this reason, the purpose of the research was to develop and demonstrate the feasibility of an artificial intelligence (AI)-based smart monitoring system that will effectively enable water operators to ensure a quasi real-time quality control for early chemical and/or bio-contamination detection and preemptive risk management. Advanced pattern recognizers, such as Support Vector Machines (SVMs), and innovative sensing technology solutions, such as Artificial Neural Network (ANN), have been used for this purpose, identifying the anomalies and the severity-level assessment.",
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