Modeling streamflow time series using nonlinear SETAR-GARCH models

Fathian, Farshad; Fard, Ahmad Fakheri; Ouarda, Taha B. M. J.; Dinpashoh, Yagob; Nadoushani, S. Saeid Mousavi

doi:10.1016/j.jhydrol.2019.03.072

Cited by 28 publications

(13 citation statements)

References 31 publications

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“…On the one hand, as expressed earlier, streamflow and other hydrological variables have a nonlinear behavior, so that in modeling experts should consider both deterministic (algebraic) and stochastic parts of this parameter in order to make a proper decision for water resources management purposes [48]. According to the above study results, the stochastic part of streamflow has extremely improved the performance of the sole-models, i.e., OPELM and CHAID, in terms of prediction.…”

Section: Discussionmentioning

confidence: 69%

“…By applying nonlinear models to hydrologic processes, especially streamflow modeling, it is worth mentioning that the conventional linear models primarily focus on the average of data (first-order moment) because they do not consider the second moment of data (variance), thus, their application is not enough for modeling stochastic data. By using linear models, experts cannot seize the nonlinear characteristics of hydrological data [48]. Besides, methods for working with changes to variance over time are necessary for water resources management developments [50].…”

Section: Arch-type Modelsmentioning

confidence: 99%

“…Fathian et al developed a nonlinear hybrid time-series, namely SETAR-GARCH, generalized autoregressive conditional heteroscedasticity, modeling streamflow in the case of Zarrineh Rood River at Urmia Lake Basin (ULB). They found that SETAR-GARCH models performed better than the models without GARCH, showing that the hybrid method performed better in modeling than sole models [48]. However, the integration of AI and time-series methods with accuracy and optimum results is still scarce for hydrological phenomena such as streamflow.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 69%

Section: Arch-type Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

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“…Recently developments in nonlinear time series modeling by researchers could be divided into considering the structural asymmetry of models in conditional mean and kurtosis. Nonlinear time series has received much attention in recent years (Fathian et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Szolgayová et al developed a hybrid model for forecasting daily river discharges of the Hron and Morava rivers in Slovakia by using the generalized autoregressive conditional heteroscedasticity (GARCH) model (Szolgayová et al, 2017). Based on the literature, threshold time series along with conditional heteroscedasticity models in the case of streamflow modeling are still novel nonlinear techniques which they help the precious and rigorous streamflow predicting models (Fathian et al, 2019).…”

Section: Introductionmentioning

confidence: 99%