Publication:
Implementation of machine learning methods for monitoring and predicting water quality parameters

Simple item page
dc.citedby10
dc.contributor.authorHayder G.en_US
dc.contributor.authorKurniawan I.en_US
dc.contributor.authorMustafa H.M.en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid56541431000en_US
dc.contributor.authorid57217195204en_US
dc.date.accessioned2023-05-29T09:12:47Z
dc.date.available2023-05-29T09:12:47Z
dc.date.issued2021
dc.description.abstractThe importance of good water quality for human use and consumption can never be underestimated, and its quality is determined through effective monitoring of the water quality index. Different approaches have been employed in the treatment and monitoring of water quality parameters (WQP). Presently, water quality is carried out through laboratory experiments, which requires costly reagents, skilled labor, and consumes time. Thereby making it necessary to search for an alternative method. Recently, machine learning tools have been successfully implemented in the monitoring, estimation, and predictions of river water quality index to provide an alternative solution to the limitations of laboratory analytical methods. In this study, the potentials of one of the machine learning tools (artificial neural network) were explored in the predictions and estimation of the Kelantan River basin. Water quality data collected from the 14 stations of the River basin was used for modeling and predicting (WQP). As for WQP analysis, the results obtained from this study show that the best prediction was obtained from the prediction of pH. The low kurtosis values of pH indicate that the appearance of outliers give a negative impact on the performance. As for WQP analysis for each station, we found that the WQP prediction in station 1, 2, and 3 give the good results. This is related to the available data of those stations that are more than the available data in other stations, except station 8. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.33263/BRIAC112.92859295
dc.identifier.epage9295
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85091132978
dc.identifier.spage9285
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091132978&doi=10.33263%2fBRIAC112.92859295&partnerID=40&md5=a21dcd384728ca33db328e207d3476f1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26613
dc.identifier.volume11
dc.publisherAMG Transcend Associationen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleBiointerface Research in Applied Chemistry
dc.titleImplementation of machine learning methods for monitoring and predicting water quality parametersen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS