Publication:
Predicting Water Quality Parameters in a Complex River System

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dc.citedby6
dc.contributor.authorKurniawan I.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorMustafa H.M.en_US
dc.contributor.authorid56541431000en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57217195204en_US
dc.date.accessioned2023-05-29T08:12:02Z
dc.date.available2023-05-29T08:12:02Z
dc.date.issued2020
dc.description.abstractThis research applied a machine learning technique for predicting the water quality parameters of Kelantan River?using the historical data collected from various stations. Support Vector Machine (SVM) was used to develop the?prediction model. Six water quality parameters (dissolved oxygen (DO), biochemical oxygen demand (BOD),?chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and suspended solids (SS)) were predicted. The?dataset was obtained from the measurement of 14 stations of Kelantan River from September 2005 to December?2017 with a total sample of 148 monthly data. We defined 3 schemes of prediction to investigate the contribution?of the attribute number and the model performance. The outcome of the study demonstrated that the prediction?of the suspended solid parameter gave the best performance, which was indicated by the highest values of the?R2 score. Meanwhile, the prediction of the COD parameter gave the lowest score of R2 score, indicating the difficulty?of the dataset to be modelled by SVM. The analysis of the contribution of attribute number shows that the?prediction of the four parameters (DO, BOD, NH3-N, and SS) is directly proportional to the performance of the?model. Similarly, the best prediction of the pH parameter is obtained from the utilization of the least number of?attributes found in scheme 1. � 2020. The American Society of Hematology. All Rights Reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.12911/22998993/129579
dc.identifier.epage257
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85098595909
dc.identifier.spage250
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85098595909&doi=10.12911%2f22998993%2f129579&partnerID=40&md5=e165ee48dc91f9f1eb4bd431d5dd7135
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25635
dc.identifier.volume22
dc.publisherPolish Society of Ecological Engineering (PTIE)en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleJournal of Ecological Engineering
dc.titlePredicting Water Quality Parameters in a Complex River Systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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