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Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

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dc.citedby54
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorYaseen Z.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorKoting S.B.en_US
dc.contributor.authorSalih S.Q.en_US
dc.contributor.authorMohtar W.H.M.W.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid57207789882en_US
dc.contributor.authorid56436206700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid55839645200en_US
dc.contributor.authorid57203978808en_US
dc.contributor.authorid57215829072en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:06:57Z
dc.date.available2023-05-29T08:06:57Z
dc.date.issued2020
dc.descriptionarticle; feasibility study; forecasting; genetic algorithm; radial basis function neural network; river; time series analysisen_US
dc.description.abstractIn nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting. � 2020, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4684
dc.identifier.doi10.1038/s41598-020-61355-x
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85082004774
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082004774&doi=10.1038%2fs41598-020-61355-x&partnerID=40&md5=eef3d942bed082e2de92783876a66731
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25143
dc.identifier.volume10
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleInput attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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