Publication:
Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan

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dc.citedby4
dc.contributor.authorZiyad Sami B.F.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorMurti M.A.en_US
dc.contributor.authorSuhendi A.en_US
dc.contributor.authorZiyad Sami B.H.en_US
dc.contributor.authorWong J.K.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57481286200en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid24734366700en_US
dc.contributor.authorid18438698200en_US
dc.contributor.authorid57481263600en_US
dc.contributor.authorid57194870148en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:36:06Z
dc.date.available2023-05-29T09:36:06Z
dc.date.issued2022
dc.descriptionoxygen; algorithm; environmental monitoring; machine learning; procedures; reproducibility; Taiwan; water quality; Algorithms; Environmental Monitoring; Machine Learning; Oxygen; Reproducibility of Results; Taiwan; Water Qualityen_US
dc.description.abstractWater quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it�s the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3649
dc.identifier.doi10.1038/s41598-022-06969-z
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85125980788
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125980788&doi=10.1038%2fs41598-022-06969-z&partnerID=40&md5=5822e0099326742cdd6ccae4711ea6da
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26662
dc.identifier.volume12
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleMachine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwanen_US
dc.typeArticleen_US
dspace.entity.typePublication
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