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Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan

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dc.citedby3
dc.contributor.authorAzha S.F.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorAhmad Z.en_US
dc.contributor.authorZhang J.en_US
dc.contributor.authorBasri H.en_US
dc.contributor.authorZawawi M.H.en_US
dc.contributor.authorNoh N.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid56166798400en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid55276521200en_US
dc.contributor.authorid47562444500en_US
dc.contributor.authorid57065823300en_US
dc.contributor.authorid39162217600en_US
dc.contributor.authorid57205236493en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2024-10-14T03:17:24Z
dc.date.available2024-10-14T03:17:24Z
dc.date.issued2023
dc.description.abstractThe escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climate fluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands of individuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have been established worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by the intricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robust water quality prediction model. The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. In this particular investigation, machine learning model called Feedforward Artificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River, Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether to include or exclude parameters such as BOD and COD, which are not measured in real time and can be costly to monitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in three different scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the third scenario excluded both BOD and COD. It was suggested that the model has better predictive power between input variables and output variables. Factor contributed to river pollution has been identified and mitigation plan for Batu Pahat river pollution has been proposed. This could provide an effective alternative to compute the pollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems. � 2023 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo111190
dc.identifier.doi10.1016/j.ecolind.2023.111190
dc.identifier.scopus2-s2.0-85175810279
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85175810279&doi=10.1016%2fj.ecolind.2023.111190&partnerID=40&md5=9754f7da26194d156484852ca6528b67
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33899
dc.identifier.volume156
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleEcological Indicators
dc.subjectBatu Pahat River
dc.subjectFeedforward artificial neural network
dc.subjectMitigation plans
dc.subjectSDG
dc.subjectWater Energy Security
dc.subjectWater quality index
dc.subjectBatu Pahat
dc.subjectJohor
dc.subjectMalaysia
dc.subjectWest Malaysia
dc.subjectClimate change
dc.subjectEcosystems
dc.subjectEnergy security
dc.subjectFeedforward neural networks
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectNetwork security
dc.subjectRiver pollution
dc.subjectWater quality
dc.subjectBatu pahat river
dc.subjectFeed-forward artificial neural networks
dc.subjectHealth monitoring
dc.subjectMitigation plans
dc.subjectRiver health
dc.subjectSDG
dc.subjectWater energy
dc.subjectWater energy security
dc.subjectWater quality indexes
dc.subjectWater quality parameters
dc.subjectartificial neural network
dc.subjectclimate change
dc.subjectmachine learning
dc.subjectriver pollution
dc.subjectwater planning
dc.subjectwater quality
dc.subjectwater resource
dc.subjectRivers
dc.titleEnhancing river health monitoring: Developing a reliable predictive model and mitigation planen_US
dc.typeArticleen_US
dspace.entity.typePublication
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