Attar, Nasrin Fathollahzadeh and Pham, Quoc Bao and Nowbandegani, Sajad Fani and Rezaie-Balf, Mohammad and Fai, Chow Ming and Ahmed, Ali Najah and Pipelzadeh, Saeed and Dung, Tran Duc and Nhi, Pham Thi Thao and Khoi, Dao Nguyen and El-Shafie, Ahmed (2020) Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake basin based upon the autoregressive conditionally heteroskedastic time-series model. Applied Sciences-Basel, 10 (2). ISSN 2076-3417, DOI https://doi.org/10.3390/app10020571.
Full text not available from this repository.Abstract
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 m(3)/s, NSE = 0.92, MAE = 0.719 m(3)/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m(3)/s, NSE = 0.86, MAE = 1.467 m(3)/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.
| Item Type: | Article |
|---|---|
| Funders: | Innovation & Research Management Center (iRMC), Universiti Tenaga Nasional (UNITEN), Malaysia [Grant No:RJO: 10436494] |
| Uncontrolled Keywords: | Data-driven models; Monthly streamflow; Arch-type hybrid models; Urmia Lake Basin |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 28 Nov 2023 05:00 |
| Last Modified: | 28 Nov 2023 05:00 |
| URI: | http://eprints.um.edu.my/id/eprint/36985 |
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