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Support vector machine and neural network based model for monthly stream flow forecasting

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dc.citedby2
dc.contributor.authorZaini N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorYusoff M.en_US
dc.contributor.authorOsmi S.F.C.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.authorid54963643200en_US
dc.contributor.authorid57190171141en_US
dc.contributor.authorid54581400300en_US
dc.date.accessioned2023-05-29T06:54:58Z
dc.date.available2023-05-29T06:54:58Z
dc.date.issued2018
dc.description.abstractAccurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm - backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R 2 ) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R 2 for hybrid SVM-PSO are 24.8267 m 3 /s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m 3 /s and 0.9305 of R 2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m 3 /s and R 2 , 0.7740 while BPNN model produces lower RMSE and R 2 of 28.1396 m 3 /s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting. � 2018 Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14419/ijet.v7i4.35.23089
dc.identifier.epage688
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85059232933
dc.identifier.spage683
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059232933&doi=10.14419%2fijet.v7i4.35.23089&partnerID=40&md5=e203a6dcae5ca23ac9f92097b831125c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24063
dc.identifier.volume7
dc.publisherScience Publishing Corporation Incen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Engineering and Technology(UAE)
dc.titleSupport vector machine and neural network based model for monthly stream flow forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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