Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow
Data 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;...
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2023
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my.uniten.dspace-254102023-05-29T16:09:05Z Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow Osman A. Afan H.A. Allawi M.F. Jaafar O. Noureldin A. Hamzah F.M. Ahmed A.N. El-shafie A. 56216559200 56436626600 57057678400 6504503295 7003905060 56266163500 57214837520 16068189400 Data 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 River Data-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. Final 2023-05-29T08:09:05Z 2023-05-29T08:09:05Z 2020 Article 10.1016/j.jhydrol.2020.124896 2-s2.0-85082678553 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082678553&doi=10.1016%2fj.jhydrol.2020.124896&partnerID=40&md5=fe93257950047df90013825140a0551d https://irepository.uniten.edu.my/handle/123456789/25410 586 124896 Elsevier B.V. Scopus |
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Data 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 River |
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56216559200 |
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56216559200 Osman A. Afan H.A. Allawi M.F. Jaafar O. Noureldin A. Hamzah F.M. Ahmed A.N. El-shafie A. |
| format |
Article |
| author |
Osman A. Afan H.A. Allawi M.F. Jaafar O. Noureldin A. Hamzah F.M. Ahmed A.N. El-shafie A. |
| spellingShingle |
Osman A. Afan H.A. Allawi M.F. Jaafar O. Noureldin A. Hamzah F.M. Ahmed A.N. El-shafie A. Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow |
| author_sort |
Osman A. |
| title |
Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow |
| title_short |
Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow |
| title_full |
Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow |
| title_fullStr |
Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow |
| title_full_unstemmed |
Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow |
| title_sort |
adaptive fast orthogonal search (fos) algorithm for forecasting streamflow |
| publisher |
Elsevier B.V. |
| publishDate |
2023 |
| _version_ |
1806427295934578688 |
| score |
13.214268 |