A new soft computing model for daily streamflow forecasting

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dc.contributor.authorSaad Sh. Sammen
dc.contributor.authorMohammad Ehteram
dc.contributor.authorS. I. Abba
dc.contributor.authorR. A. Abdulkadir
dc.contributor.authorAli Najah Ahmed
dc.contributor.authorAhmed El-Shafie
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-20T07:04:29Z
dc.date.available2024-05-20T07:04:29Z
dc.date.issued2021
dc.description.abstractThe ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations.
dc.identifier.citationSammen, S. S., Ehteram, M., Abba, S. I., Abdulkadir, R. A., Ahmed, A. N., & El-Shafie, A. (2021). A new soft computing model for daily streamflow forecasting. Stochastic Environmental Research and Risk Assessment, 35(12), 2479-2491.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/1516
dc.language.isoen
dc.publisherSpringer
dc.publisher.alternativeStochastic Environmental Research and Risk Assessment
dc.subjectMLP
dc.subjectStreamflow
dc.subjectSunflower optimization
dc.subjectPrincipal component analysis
dc.titleA new soft computing model for daily streamflow forecasting
dc.typeJournal

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