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Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach

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dc.citedby34
dc.contributor.authorRezaie-Balf M.en_US
dc.contributor.authorAttar N.F.en_US
dc.contributor.authorMohammadzadeh A.en_US
dc.contributor.authorMurti M.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorNabipour N.en_US
dc.contributor.authorAlaghmand S.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57193900045en_US
dc.contributor.authorid57203768412en_US
dc.contributor.authorid56385332000en_US
dc.contributor.authorid24734366700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57209908854en_US
dc.contributor.authorid55193594200en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:07:14Z
dc.date.available2023-05-29T08:07:14Z
dc.date.issued2020
dc.descriptionForecasting; Kalman filters; Mean square error; Neural networks; Quality assurance; Water conservation; Water management; Water quality; Water supply; Comparative assessment; Data assimilation methods; Ensemble Kalman Filter; Hydrological variables; Intrinsic time-scale decompositions; Physicochemical parameters; Preprocessing techniques; Root mean square errors; River pollutionen_US
dc.description.abstractWater quality has a crucial impact on human health; therefore, water quality index modeling is one of the challenging issues in the water sector. The accurate prediction of water quality index is an essential requisite for water quality management, human health, public consumption, and domestic uses. A comprehensive review as an initial attempt is conducted on existing solutions through data-driven models. In addition, the ensemble Kalman filter is found to be a suitable data assimilation method, which is successfully applied in hydrological variables modeling and other complexes, nonlinear, and chaotic problems. In this study, a new application of ensemble Kalman filter-artificial neural network is proposed to predict water quality index using physicochemical parameters for two commonly pollutant rivers, namely Klang and Langat, in Malaysia. As a further attempt, in order to improve the models� performance, a new preprocessing technique is adopted as the newly constructed assimilated model. The results confirm that ensemble hybrid based intrinsic time-scale decomposition has reduced root mean square error by 24% for Klang and 34% for Langat, respectively, compared with the intrinsic time-scale decomposition-conventional neural network model. Overall, the developed assimilated methodology shows the robustness of the proposed ensemble hybrid model in analyzing water quality index over monthly horizons that experts could evaluate the water quality of rivers more efficiently. � 2020 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo122576
dc.identifier.doi10.1016/j.jclepro.2020.122576
dc.identifier.scopus2-s2.0-85087633860
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087633860&doi=10.1016%2fj.jclepro.2020.122576&partnerID=40&md5=750af72f3fb24d8eda949522d3aeb19f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25191
dc.identifier.volume271
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleJournal of Cleaner Production
dc.titlePhysicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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