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Applications of IoT and Artificial Intelligence in Water Quality Monitoring and Prediction: A Review

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dc.citedby13
dc.contributor.authorMustafa H.M.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorSalisu A.en_US
dc.contributor.authorid57217195204en_US
dc.contributor.authorid57222385387en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid56587770200en_US
dc.date.accessioned2023-05-29T09:09:22Z
dc.date.available2023-05-29T09:09:22Z
dc.date.issued2021
dc.descriptionBackpropagation; Decision making; Forecasting; Gradient methods; Linear systems; Nonlinear programming; Predictive analytics; Radial basis function networks; Sewage treatment plants; Wastewater treatment; Water quality; Broyden-Fletcher-Goldfarb-Shanno; Feedforward backpropagation; Internet of Things (IOT); Modeling and forecasting; Radial basis function neural networks; Wastewater treatment plants; Water quality monitoring; Water quality parameters; Internet of thingsen_US
dc.description.abstractCurrently, internet of things (IoT) devices like environmental sensors are used to capture real-time data that can be viewed and interpreted via a visual format supported by a server computer. However, to facilitate modeling and forecasting, artificial intelligence (AI) techniques are effective in statistically analyzing complex non-linear systems and a large amount of historical data series within a short period. This present review article covers selected research journals published from 2014 to 2020. The findings from previous research indicate that despite the limitations of artificial neural network (ANN) tools, ANN has proved to be useful and powerful techniques that can be used in the field of hydrology. Similarly, ANN tools have the ability to evaluate historical data collected from different river stations and wastewater treatment plants with minimum errors within a short time. Therefore, based on the selected past literature used for this review we found that different types of ANN algorithm such as feed-forward backpropagation (FFBP) algorithm, gradient descent, Broyden-Fletcher-Goldfarb-Shanno (BFGS), conjugate gradient, radial basis function neural networks (RBFNN), neural network fitting (NNF), cascade forward back propagation (CFBP), ensemble ANN (EANN) and single AAN (SANN) have been employed in the prediction and monitoring of water quality parameters with satisfactory outcome. Furthermore, modeling alongside forecasting of water quality parameters would act as a big leap for government agencies and independent organisations in monitoring, decision making and regulating waste discharged into natural water bodies in order to achieve a safe and improved water quality for users. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9358675
dc.identifier.doi10.1109/ICICT50816.2021.9358675
dc.identifier.epage975
dc.identifier.scopus2-s2.0-85102597790
dc.identifier.spage968
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102597790&doi=10.1109%2fICICT50816.2021.9358675&partnerID=40&md5=f0a9ceac812797c5b8dc5e43cb8ad613
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26346
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021
dc.titleApplications of IoT and Artificial Intelligence in Water Quality Monitoring and Prediction: A Reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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