Real-Time Monitoring and Controlling of Water Quality in Water Distribution Networks Based on Flow Cytometry and Fluorescence Spectroscopy

Schuster, Jonas; Kadinski, Leonid; Cao, Hao; Abhijith, Gopinathan R.; Grieb, Anissa; Li, Pu; Ostfeld, Avi; Ernst, Mathias

doi:10.1061/9780784484258.108

Cited by 3 publications

(1 citation statement)

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“…Monitoring organic parameter usually requires timeintensive laboratory tests and the results are not available at once. Schuster et al (2022) [6] describe an innovative, sensitive and low-cost method how to determine organic compounds in drinking water with fluorescence sensors and flow cytometers. Recent studies have used machine learning methodologies to detect water quality anomalies and their sources by training these algorithms with specific water quality parameters to predict possible water events.…”

Section: Introductionmentioning

confidence: 99%

Machine learning methodologies to predict possible water quality anomalies as a support tool for online monitoring of organic parameters

Kadinski,

Schuster,

Abhijith

et al. 2022

Proceedings - 2nd International Join Conference on Water Distribution System Analysis (WDSA)& Computing and Control in the

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Water Distribution Systems (WDSs) function to deliver high-quality water in major quantities. While standard water quality parameters are monitored at waterworks, it is still a challenge to monitor water quality in the WDS network itself. While mostly hydraulic parameters are frequently monitored and modelled in drinking water networks in Germany, the measurements of specific organic and bacteriological water quality parameter are still done offline which can take hours or even days which might be too late to react to possible water events. This study utilizes water quality data of a Utility in Hamburg, Germany to train machine learning algorithms to predict possible anomalies in specific water quality parameters which can indicate the necessity for more thorough investigations. While a large amount of water parameters is utilized and checked for deviations from the normal distribution, the input features to train the machine learning algorithms are just parameters which can be measured online like pH, temperature, total cell count of bacteria and the organic content of the water sample. A parallel study uses innovative online testing methods like fluorescence spectroscopy and flow cytometry in batch and flow experiments with the overarching goal of validating the trained algorithm to develop a wholesome online monitoring and warning system for drinking water anomalies. Various algorithms like Random Forest, Gradient Boosting, Decision Tree, Logistic Regression and Artificial Neural Networks are trained to predict whether the water samples indicate possible water quality anomalies. First results of this study show promising possibilities for a data driven online water quality prediction methodology which can help to digitalize the water sector immensely.

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Section: Introductionmentioning

confidence: 99%