A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring

Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition t...

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Vydáno v:Water (Basel) Ročník 14; číslo 9; s. 1384
Hlavní autoři: Lowe, Matthew, Qin, Ruwen, Mao, Xinwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2022
Témata:
Adsorption
Analysis
Artificial intelligence
Cost control
Information management
Internet of Things
Machine learning
Neural networks
Purification
Sensors
Sewage
Support vector machines
Technology
Water quality
Water treatment
Germany
ISSN:2073-4441, 2073-4441
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Shrnutí:Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition to providing computer-assisted aid to complex issues surrounding water chemistry and physical/biological processes, artificial intelligence and machine-learning (AI/ML) applications are anticipated to further optimize water-based applications and decrease capital expenses. This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring, water-quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring. Although success in control, optimization, and modeling has been achieved with the AI methods, ML models, and smart technologies (including the Internet of Things (IoT), sensors, and systems based on these technologies) that are reviewed herein, key challenges and limitations were common and pervasive throughout. Poor data management, low explainability, poor model reproducibility and standardization, as well as a lack of academic transparency are all important hurdles to overcome in order to successfully implement these intelligent applications. Recommendations to aid explainability, data management, reproducibility, and model causality are offered in order to overcome these hurdles and continue the successful implementation of these powerful tools.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w14091384