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Rainfall-runoff forecasting utilizing genetic programming technique

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dc.citedby4
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorRahman R.A.B.A.en_US
dc.contributor.authorBorhana A.A.en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57205651379en_US
dc.contributor.authorid55212152300en_US
dc.date.accessioned2023-05-29T07:30:46Z
dc.date.available2023-05-29T07:30:46Z
dc.date.issued2019
dc.description.abstractThis paper reports how the rainfall-runoff is forecasted utilizing Genetic Programming (GP) technique. It is a program that was inspired by biological processes such as mutation, crossover, and inversion in order to create a new generation. It is a program that will learn and improve with each analysis done. It uses a trial an error method in order to forecast rainfall-runoff. GP uses Root Mean Squared Error (RMSE) as an indication of how accurate the results of the forecast. The lower and closer the RMSE to zero, the more accurate the rainfall-runoff forecasted. The study consists of running the data on the software until the lowest RMSE is obtained. This research contains three models which use a different number of input variables to see whether it will give an impact on the rainfall-runoff forecasting. The results are compared and a bar chart is plotted. � IAEME Publication.en_US
dc.description.natureFinalen_US
dc.identifier.epage1534
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85060928940
dc.identifier.spage1523
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060928940&partnerID=40&md5=fc57e4d103c43b26a882d93d5d621a62
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25035
dc.identifier.volume10
dc.publisherIAEME Publicationen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Civil Engineering and Technology
dc.titleRainfall-runoff forecasting utilizing genetic programming techniqueen_US
dc.typeArticleen_US
dspace.entity.typePublication
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