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Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

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dc.citedby45
dc.contributor.authorAbba S.I.en_US
dc.contributor.authorPham Q.B.en_US
dc.contributor.authorSaini G.en_US
dc.contributor.authorLinh N.T.T.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMohajane M.en_US
dc.contributor.authorKhaledian M.en_US
dc.contributor.authorAbdulkadir R.A.en_US
dc.contributor.authorBach Q.-V.en_US
dc.contributor.authorid57208942739en_US
dc.contributor.authorid57208495034en_US
dc.contributor.authorid57197592021en_US
dc.contributor.authorid57211268069en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57195618368en_US
dc.contributor.authorid23089044300en_US
dc.contributor.authorid57200567560en_US
dc.contributor.authorid23033338600en_US
dc.date.accessioned2023-05-29T08:07:12Z
dc.date.available2023-05-29T08:07:12Z
dc.date.issued2020
dc.descriptionammonia; artificial neural network; back propagation; biochemical oxygen demand; dissolved oxygen; nonlinearity; parameterization; prediction; regression analysis; water quality; India; Yamuna River; fuzzy logic; India; intelligence; machine learning; river; water quality; Fuzzy Logic; India; Intelligence; Machine Learning; Rivers; Water Qualityen_US
dc.description.abstractIn recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square�(RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources. � 2020, Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-020-09689-x
dc.identifier.epage41539
dc.identifier.issue33
dc.identifier.scopus2-s2.0-85088160474
dc.identifier.spage41524
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85088160474&doi=10.1007%2fs11356-020-09689-x&partnerID=40&md5=85e337a9a604c7373a4df0ba4ad58a78
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25185
dc.identifier.volume27
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleImplementation of data intelligence models coupled with ensemble machine learning for prediction of water quality indexen_US
dc.typeArticleen_US
dspace.entity.typePublication
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