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Integrated nonlinear autoregressive neural network and Holt winters exponential smoothing for river streaming flow forecasting at Aswan High

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dc.citedby1
dc.contributor.authorDullah H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57199323863en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:18:39Z
dc.date.available2024-10-14T03:18:39Z
dc.date.issued2023
dc.description.abstractStreamflow forecasting process exhibited highly nonstationary and stochastic pattern, thus not easy to be done with simple models. There is a need to develop an efficient and precise streamflow forecasting system which is vital for water management at hydrological infrastructures like Aswan High Dam (AHD). As the decision makers will be able to decide on water allocation for different purposes such as irrigation, domestic and industrial uses. This study explores the potential of AI model: nonlinear autoregressive neural network (NAR) in performing inflow forecasting to AHD. The dataset of past 130�years of Nile River discharge rate was used for the network development as well as evaluation of models� performance. This study also proposes an integration process of NAR with Holt-Winters exponential smoothing to improve the accuracy of the model. To determine the models� performance, different indicators were employed and calculated (MAE, MAPE, RMSE, R2). The results were compared to identify the optimal network architecture. The results show that the NAR models are capable of predicting the future values of AHD inflow in monthly time steps accurately. For standard NAR model, the root mean squared error (RMSE) was 2.0072, and the coefficient of determination (R2) between recorded and forecasted values was 0.9152. Values of RMSE = 1.5421 and R2 = 0.9760 and RMSE = 1.0843 and R2 = 0.9823 were obtained by NAR-SES and NAR-HW models respectively. The results reveal that combination of Holt-Winters exponential smoothing with NAR significantly improved the precision beyond the standard model. This study proved that NAR neural networks can be useful to address streamflow forecasting problems. � 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s12145-022-00913-5
dc.identifier.epage786
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85143726590
dc.identifier.spage773
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85143726590&doi=10.1007%2fs12145-022-00913-5&partnerID=40&md5=ad76e88f67c0e0b6183a196136afc723
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34253
dc.identifier.volume16
dc.pagecount13
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEarth Science Informatics
dc.subjectAswan high dam
dc.subjectExponential smoothing
dc.subjectForecasting
dc.subjectHolt-winters
dc.subjectMachine learning
dc.subjectNonlinear autoregressive neural network
dc.subjectAswan Dam
dc.subjectNile River
dc.subjectartificial neural network
dc.subjectdam
dc.subjectforecasting method
dc.subjectinflow
dc.subjectnonlinearity
dc.subjectriver discharge
dc.subjectsmoothing
dc.subjectstreamflow
dc.subjectwinter
dc.titleIntegrated nonlinear autoregressive neural network and Holt winters exponential smoothing for river streaming flow forecasting at Aswan Highen_US
dc.typeArticleen_US
dspace.entity.typePublication
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