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The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction

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dc.citedby17
dc.contributor.authorAdnan R.M.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorMostafa R.R.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid56689592000en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid57216628949en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:41:55Z
dc.date.available2023-05-29T09:41:55Z
dc.date.issued2022
dc.descriptionBalancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Support vector regressions; Support vectors machine; Simulated annealing; algorithm; mayfly; optimization; prediction; streamflow; support vector machine; Jhelum Riveren_US
dc.description.abstractThis paper focuses on the development of a robust accurate streamflow prediction model by balancing the abilities of exploitation and exploration to find the best parameters of a machine learning model. To do so, the simulated annealing (SA) algorithm is integrated with the mayfly optimization algorithm (MOA) as SAMOA to determine the optimal hyper-parameters of support vector regression (SVR) to overcome the exploration weakness of the MOA method. The proposed method is compared with the classical SVR and hybrid SVR-MOA. To examine the accuracy of the selected methods, monthly hydroclimatic data from Jhelum River Basin is used to predict the monthly streamflow on the basis of RMSE, MAE, NSE, and R2 indices. Test results show that the SVR-SAMOA outperformed the SVR-MOA and SVR models. SVR-SAMOA reduced the prediction errors of the SVR-MOA and SVR models by decreasing the RMSE and the MSE from 21.4% to 14.7% and from 21.7% to 15.1%, respectively, in the test stage. � 2022 IAHS.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/02626667.2021.2012182
dc.identifier.epage174
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85123408754
dc.identifier.spage161
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123408754&doi=10.1080%2f02626667.2021.2012182&partnerID=40&md5=6dfa661d63bd74946ae878224168fff8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27271
dc.identifier.volume67
dc.publisherTaylor and Francis Ltd.en_US
dc.sourceScopus
dc.sourcetitleHydrological Sciences Journal
dc.titleThe potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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