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Article

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

1
Water Engineering Department, Urmia University, Urmia 5756151818, Iran
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Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, Tainan 701, Taiwan
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Department of Civil Engineering, Graduate University of Advanced Technology, Kerman 76315-116, Iran
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Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
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Institute of Energy Infrastructure (IEI), Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia
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Department of Civil Engineering, Islamic Azad University Arak, Branch Arak 38135-567, Iran
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Centre of Water Management and Climate Change, Vietnam National University Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
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Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
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Faculty of Environment, University of Science, Vietnam National University Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
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Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 571; https://doi.org/10.3390/app10020571
Received: 11 October 2019 / Revised: 2 December 2019 / Accepted: 20 December 2019 / Published: 13 January 2020
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. View Full-Text
Keywords: Data-Driven Models; monthly streamflow; ARCH-type hybrid models; Urmia Lake Basin Data-Driven Models; monthly streamflow; ARCH-type hybrid models; Urmia Lake Basin
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MDPI and ACS Style

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. Appl. Sci. 2020, 10, 571. https://doi.org/10.3390/app10020571

AMA Style

Attar NF, Pham QB, Nowbandegani SF, Rezaie-Balf M, Fai CM, Ahmed AN, Pipelzadeh S, Dung TD, Nhi PTT, Khoi DN, 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. Applied Sciences. 2020; 10(2):571. https://doi.org/10.3390/app10020571

Chicago/Turabian Style

Attar, Nasrin Fathollahzadeh, Quoc Bao Pham, Sajad Fani Nowbandegani, Mohammad Rezaie-Balf, Chow Ming Fai, Ali Najah Ahmed, Saeed Pipelzadeh, Tran Duc Dung, Pham Thi Thao Nhi, Dao Nguyen Khoi, and Ahmed El-Shafie. 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 10, no. 2: 571. https://doi.org/10.3390/app10020571

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