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Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications

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dc.citedby22
dc.contributor.authorIrwan D.en_US
dc.contributor.authorAli M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorJacky G.en_US
dc.contributor.authorNurhakim A.en_US
dc.contributor.authorPing Han M.C.en_US
dc.contributor.authorAlDahoul N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid55937632900en_US
dc.contributor.authorid57115742100en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid58360807500en_US
dc.contributor.authorid58362682400en_US
dc.contributor.authorid58364582800en_US
dc.contributor.authorid56656478800en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:37Z
dc.date.available2024-10-14T03:17:37Z
dc.date.issued2023
dc.description.abstractThe water is the main pivotal sources of irrigation in agricultural activities and affects human daily activities such as drinking. The water quality has a significant impact on various aspects and thus this review aims to addresses existing problems related to water quality prediction methods that have been found in the literature. We explore numerous quality parameters incorporated in the modelling process to measure the quality of water. Furthermore, we review the commonly adopted artificial intelligence-based models which have been utilized to forecast the water quality. 83 studies published from 2009 to 2023 were selected and reviewed based on their success in modelling and forecasting the water quality in multiple regions. We compared these articles in terms of parameters, modelling algorithms, time scale scenarios, and performance measurement indicators. This paper is beneficial to researchers that have interests to conduct future studies related to water quality forecasting. Additionally, we discuss a variety of modelling methods such as deep learning (DL) that have proven to boost the efficiency compared to traditional machine learning (ML) models. As a result, the hybrid-DL models were found to outperform other models such as standalone ML, standalone DL, and hybrid-ML. This study shows a significant limitation of the data-hungry DL models which require a big data size for modelling. Hence, at the end of this review study, we discuss the potential of some methods such as generative adversarial networks (GANs) and attention-based transformer to open the door for water quality prediction improvement. GAN has shown promising performance in other domains for synthetic data generation. The potential usage of GAN for water quality domain can overcome the limitations of lack of data and enhance the performance of the predictive models reviewed in this study. Similarly, transformer was found to be state of the art model for time series prediction and thus it can be good candidate to predict water quality. � 2023, The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE).en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11831-023-09947-4
dc.identifier.epage4652
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85163107823
dc.identifier.spage4633
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85163107823&doi=10.1007%2fs11831-023-09947-4&partnerID=40&md5=2ce6168ff221a88b10efda8fb5aceaab
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34001
dc.identifier.volume30
dc.pagecount19
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleArchives of Computational Methods in Engineering
dc.subjectDeep learning
dc.subjectForecasting
dc.subjectGenerative adversarial networks
dc.subjectLearning systems
dc.subjectNumerical methods
dc.subjectPotable water
dc.subjectAgricultural activities
dc.subjectDaily activity
dc.subjectExisting problems
dc.subjectLearning models
dc.subjectModeling process
dc.subjectPerformance
dc.subjectPrediction methods
dc.subjectQuality of water
dc.subjectQuality parameters
dc.subjectWater quality predictions
dc.subjectWater quality
dc.titlePredicting Water Quality with Artificial Intelligence: A Review of Methods and Applicationsen_US
dc.typeReviewen_US
dspace.entity.typePublication
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