Cyber-physical systems in water management and governance

Alexandra, Carla; Daniell, Katherine A.; Guillaume, Joseph H. A.; Saraswat, Chitresh; Feldman, Hannah R

doi:10.1016/j.cosust.2023.101290

Cited by 8 publications

(1 citation statement)

References 69 publications

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“…The use of cyber-physical systems and various sensor-based water quality monitoring systems, e.g., physical monitoring sensors, optical remote sensors and real-time monitoring sensors, in conjunction with algorithms like neural networks, support vector machines, and long short-term memory (LSTM) deep neural networks, can provide reliable solutions for water quality monitoring, management and governance. These combinations can also contribute to effectively managing data related to water quality monitoring, including time series, models, uncertainties and errors, while simultaneously improving the predictive capabilities of machine learning models [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009–2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

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Section: Introductionmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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Mutual Information Based Ensemble Anomaly Detection Strategy for Cyber-Physical Systems

Ahmad,

Petrovski,

Kotenko

et al. 2024

Lecture Notes in Networks and Systems

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Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Ganthavee,

Trzcinski

2024

Environ Chem Lett

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The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters.

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Cyber-physical systems in water management and governance

Cited by 8 publications

References 69 publications

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Mutual Information Based Ensemble Anomaly Detection Strategy for Cyber-Physical Systems

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

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