Publication:
River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach

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dc.citedby3
dc.contributor.authorRizal N.N.M.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorid57654708600en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid16023225600en_US
dc.date.accessioned2024-10-14T03:20:33Z
dc.date.available2024-10-14T03:20:33Z
dc.date.issued2023
dc.description.abstractIn depth research about river water qualities are no more outlandish nowadays due to river water pollutions and contaminations. In order to have an accurate and precise measurement taken towards these river water pollution, advanced and new technologies need to be applied rather than old technique of everyday lab testing. Therefore, with the usage of deep learning predictive models approach, the decision makers able to provide immediate response and give precautionary measures to prevent a disastrous event. In the current research, Adaptive Neuro-fuzzy Inference System (ANFIS) has been used to predict six different types of river water quality parameters at Langat River, Malaysia. Root mean square error (RMSE) and determination of coefficient (R2) were used to assess the performances of the models. The results have been proven that ANFIS able to predict the parameters of river water quality as ANFIS Model 5 has achieved the highest value of R2 (0.9712). It also obtained the low values of RMSE which were 0.0028, 0.0144 and 0.0924 for training, testing and checking data sets, respectively. Overall, the six ANFIS models have successfully predict six different water quality parameters. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-26580-8_5
dc.identifier.epage29
dc.identifier.scopus2-s2.0-85161600571
dc.identifier.spage25
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161600571&doi=10.1007%2f978-3-031-26580-8_5&partnerID=40&md5=970aef1335ace964658e737a6438d362
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34545
dc.pagecount4
dc.publisherSpringer Natureen_US
dc.sourceScopus
dc.sourcetitleAdvances in Science, Technology and Innovation
dc.subjectANFIS
dc.subjectDeep learning
dc.subjectRiver
dc.subjectRiver water quality prediction
dc.subjectWater quality
dc.titleRiver Water Quality Prediction and Analysis�Deep Learning Predictive Models Approachen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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