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Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model

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dc.citedby20
dc.contributor.authorAttar N.F.en_US
dc.contributor.authorPham Q.B.en_US
dc.contributor.authorNowbandegani S.F.en_US
dc.contributor.authorRezaie-Balf M.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorPipelzadeh S.en_US
dc.contributor.authorDung T.D.en_US
dc.contributor.authorNhi P.T.T.en_US
dc.contributor.authorKhoi D.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57203768412en_US
dc.contributor.authorid57208495034en_US
dc.contributor.authorid57208524528en_US
dc.contributor.authorid57193900045en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57215054697en_US
dc.contributor.authorid57200870280en_US
dc.contributor.authorid57200412510en_US
dc.contributor.authorid57226521007en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:14:02Z
dc.date.available2023-05-29T08:14:02Z
dc.date.issued2020
dc.description.abstractHydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid "ARCH-DDM" models outperformed standalone models in predicting monthly streamflow. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo571
dc.identifier.doi10.3390/app10020571
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85079830406
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079830406&doi=10.3390%2fapp10020571&partnerID=40&md5=7070b43fa7e6371fe6940e49649d6fb2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25766
dc.identifier.volume10
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleEnhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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