Publication:
Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization

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dc.citedby20
dc.contributor.authorZaini N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorYusoff M.en_US
dc.contributor.authorMardi N.H.en_US
dc.contributor.authorNorhisham S.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid23391662400en_US
dc.contributor.authorid57190171141en_US
dc.contributor.authorid54581400300en_US
dc.date.accessioned2023-05-29T06:52:17Z
dc.date.available2023-05-29T06:52:17Z
dc.date.issued2018
dc.descriptionAtmospheric movements; Catchments; Flood control; Forecasting; Particle swarm optimization (PSO); Rain; Rivers; Stream flow; Sustainable development; Water management; Artificial intelligence techniques; Cameron highlands; Flood prevention; Hybrid model; Hybrid support vector machines; Meteorological parameters; Model performance; River flow forecasting; Support vector machinesen_US
dc.description.abstractThe application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area. � 2018 Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12035
dc.identifier.doi10.1088/1755-1315/140/1/012035
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85046109729
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85046109729&doi=10.1088%2f1755-1315%2f140%2f1%2f012035&partnerID=40&md5=6fdd00456876b6dd9439e9abbb017925
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23840
dc.identifier.volume140
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.titleDaily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimizationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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