Data-centric intelligent systems for water quality monitoring, assessment, and control (Chapter 7).
Sanni, Samuel Eshorame Okoro, Emmanuel Emeka Sadiku, Emmanuel Rotimi Oni, Babalola Aisosa
Sanni, Samuel Eshorame
Okoro, Emmanuel Emeka
Sadiku, Emmanuel Rotimi
Oni, Babalola Aisosa
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Abstract
During the 21st century, the use of artificial intelligence (AI) has accelerated data-driven innovations that are adaptable and undoubtedly efficient in today’s digital world. This technology complements human intelligence by creating an enabling environment that delivers the ability to handle environmental issues related to water pollution. Polluted water can cause life-threatening diseases such as cholera, dysentery, and diarrhea, hence the need to harness data from water sources for use in developing useful algorithms that can accurately track and predict the influx of pollutants in waters. The recent application of high-performance data-centric systems/AI in water pollution control has resulted in apt human–machine-based expedient solutions that help to harness the potentials offered by these technologies in tackling issues that compromise water standards, and thus improve the quality of aquatic and human life. In oceanography, machine learning is being exploited as a method of monitoring marine litter via feedback-enabled algorithms. Considering the prospects underlying life sciences (robotics, machine learning, drones, and the Internet of Things), these tools can help to monitor and understand the origin, physical chemistry, and nature of water pollutants/stressors. In this chapter, topics covered include deep and machine learning applications in water quality management, modeling and prediction of surface/groundwater contamination, rainwater quality prediction using data-centric and intelligent systems, deep learning and machine learning strategies in contaminant hydrology, deep/machine learning methods in emerging contaminants and micro pollutants research, and a review of recent advances and applications of data-centric systems in water pollution monitoring, assessment, and control.
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Date
2022-01-01
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Academic Press
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Keywords
Artificial Intelligence, Data-centric systems, Feedback-enabled algorithms, Machine learning technology, Predictive modeling, Wastewater management