Publication:
Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow

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dc.citedby21
dc.contributor.authorOsman A.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorJaafar O.en_US
dc.contributor.authorNoureldin A.en_US
dc.contributor.authorHamzah F.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-shafie A.en_US
dc.contributor.authorid56216559200en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid6504503295en_US
dc.contributor.authorid7003905060en_US
dc.contributor.authorid56266163500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:09:05Z
dc.date.available2023-05-29T08:09:05Z
dc.date.issued2020
dc.descriptionData handling; Forecasting; Nonlinear systems; Regression analysis; Religious buildings; Rivers; Stochastic systems; Stream flow; Fast orthogonal searches; Forecasting accuracy; Forecasting models; High dams; Optimization modeling; Optimization scheme; Pole zero cancellation; Streamflow forecasting; Stochastic models; algorithm; artificial intelligence; hydrological modeling; identification method; optimization; river basin; streamflow; Aswan Dam; Nile Riveren_US
dc.description.abstractData-driven models for streamflow forecasting have attracted considerable attention, as they are independent of physical system features. The physical features of the river basin are extremely hard to collect, especially for large rivers. Empirical data-driven models, such as stochastic and regression models, have been widely used in the field of streamflow forecasting. However, they suffered limited accuracy in predicting extreme streamflow. They also required raw data pre-processing prior to the modeling process, especially for lengthy data records and for large time-scale increments (e.g. monthly resolution). To overcome these challenges, data-driven forecasting models based on Artificial Intelligence (AI) have been widely used and resulted in enhancing the forecasting accuracy. Nevertheless, AI-based models required augmentation with proper optimization schemes to adjust the model parameters for optimal accuracy. Furthermore, in some cases, due to unsuitability of the optimization model, there is high possibility for overfitting of the AI model, which might cause poor prediction of input patterns that were not adequately mimicked. This study introduces a new approach to streamflow forecasting based on nonlinear system identification. The proposed technique employs Fast Orthogonal Search (FOS) to develop a nonlinear model of stream flow. The main advantage of using FOS is eliminating the requirement of raw data pre-processing and the need for an optimization scheme for model parameter adjustment since the FOS algorithm takes this into account while building the model. In addition, the FOS algorithm includes a pole-zero cancellation procedure that can detect and avoid the over-fitted models. The FOS-based nonlinear modeling approach was adopted in this research for the development of a streamflow forecasting model at Aswan High Dam using monthly basis natural streamflow records for 130 years. The results indicated that the proposed FOS algorithm outperformed the previously developed AI models of streamflow forecasting for large data records and for large time-scale increment (monthly resolution). � 2020 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo124896
dc.identifier.doi10.1016/j.jhydrol.2020.124896
dc.identifier.scopus2-s2.0-85082678553
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082678553&doi=10.1016%2fj.jhydrol.2020.124896&partnerID=40&md5=fe93257950047df90013825140a0551d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25410
dc.identifier.volume586
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Hydrology
dc.titleAdaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflowen_US
dc.typeArticleen_US
dspace.entity.typePublication
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