Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model
Hydrological 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 seasonali...
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my.uniten.dspace-257662023-05-29T16:14:02Z Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model Attar N.F. Pham Q.B. Nowbandegani S.F. Rezaie-Balf M. Fai C.M. Ahmed A.N. Pipelzadeh S. Dung T.D. Nhi P.T.T. Khoi D.N. El-Shafie A. 57203768412 57208495034 57208524528 57193900045 57214146115 57214837520 57215054697 57200870280 57200412510 57226521007 16068189400 Hydrological 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. Final 2023-05-29T08:14:02Z 2023-05-29T08:14:02Z 2020 Article 10.3390/app10020571 2-s2.0-85079830406 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079830406&doi=10.3390%2fapp10020571&partnerID=40&md5=7070b43fa7e6371fe6940e49649d6fb2 https://irepository.uniten.edu.my/handle/123456789/25766 10 2 571 All Open Access, Gold MDPI AG Scopus |
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Hydrological 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. |
| author2 |
57203768412 |
| author_facet |
57203768412 Attar N.F. Pham Q.B. Nowbandegani S.F. Rezaie-Balf M. Fai C.M. Ahmed A.N. Pipelzadeh S. Dung T.D. Nhi P.T.T. Khoi D.N. El-Shafie A. |
| format |
Article |
| author |
Attar N.F. Pham Q.B. Nowbandegani S.F. Rezaie-Balf M. Fai C.M. Ahmed A.N. Pipelzadeh S. Dung T.D. Nhi P.T.T. Khoi D.N. El-Shafie A. |
| spellingShingle |
Attar N.F. Pham Q.B. Nowbandegani S.F. Rezaie-Balf M. Fai C.M. Ahmed A.N. Pipelzadeh S. Dung T.D. Nhi P.T.T. Khoi D.N. El-Shafie A. Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model |
| author_sort |
Attar N.F. |
| title |
Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model |
| title_short |
Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model |
| title_full |
Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model |
| title_fullStr |
Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model |
| title_full_unstemmed |
Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model |
| title_sort |
enhancing the prediction accuracy of data-driven models for monthly streamflow in urmia lake basin based upon the autoregressive conditionally heteroskedastic time-series model |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806424176963092480 |
| score |
13.222552 |