article · Water
Integration of the Internet of Things, artificial intelligence, and machine learning is modernising water quality prediction and wastewater management. Connected systems enable automated, real-time monitoring and process control to support global demands for clean water and sustainable operations. Documented implementations use cloud computing alongside machine learning to optimise, simulate, and automate complex processes across natural water bodies, industrial treatment facilities, and agricultural systems such as hydroponics and aquaponics. Specific applications identified across peer-reviewed evaluations include automating chlorination, managing adsorption, controlling membrane filtration, and tracking river levels. Furthermore, these smart tools help calculate water quality indices and model specific environmental parameters. Machine learning algorithms also assess treated water discharge in aquaculture setups and diverse aquatic environments, illustrating how digital platforms can improve oversight and operational efficiency across the water sector.
Growing pressure on global freshwater supplies demands more efficient, sustainable, and reliable water management methods. Combining digital sensors, cloud computing, and intelligent algorithms allows operators to detect pollution instantly, automate treatment processes, and maintain rigorous environmental standards. This technological shift helps safeguard natural aquatic ecosystems, improves agricultural productivity in systems like aquaponics, and enhances public access to safe, clean water.
The review identifies applied and tested solutions, such as automated water quality monitoring platforms and control algorithms for chlorination and membrane filtration. These technologies are relevant to municipal water authorities, aquaculture and hydroponics operators, and industrial wastewater treatment plant managers. While certain automated monitoring setups are already functioning in operational environments, broader adoption relies on integrating cloud computing and machine learning models directly into existing physical treatment infrastructures.
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Integration of the Internet of Things (IoT) into the fields of wastewater treatment and water quality prediction has the potential to revolutionize traditional approaches and address urgent challenges, considering the global demand for clean water and sustainable systems. This comprehensive article explores the transformative applications of smart IoT technologies, including artificial intelligence (AI) and machine learning (ML) models, in these areas. A successful example is the implementation of an IoT-based automated water quality monitoring system that utilizes cloud computing and ML methods to effectively address the above-mentioned issues. The IoT has been employed to optimize, simulate, and automate various aspects, such as monitoring and managing natural systems, water-treatment processes, wastewater-treatment applications, and water-related agricultural practices like hydroponics and aquaponics. This review presents a collection of significant water-based applications, which have been combined with the IoT, artificial neural networks, or ML and have undergone critical peer-reviewed assessment. These applications encompass chlorination, adsorption, membrane filtration, monitoring water quality indices, modeling water quality parameters, monitoring river levels, and automating/monitoring effluent wastewater treatment in aquaculture systems. Additionally, this review provides an overview of the IoT and discusses potential future applications, along with examples of how their algorithms have been utilized to evaluate the quality of treated water in diverse aquatic environments.
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DOI: 10.3390/w16020314
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