An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Rezaie-Balf, Mohammad; Nowbandegani, Sajad Fani; Phillips, R. C.; Fallah, Hossein; Alaghmand, Sina

doi:10.3390/w11040709

Cited by 44 publications

(21 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mode determination of the VMD technique requires careful consideration. The study in [6] utilizes a trial and error method to select mode, whereas the study in [61] employs correlation analysis for the mode determination in ensemble decomposition techniques. The present study also utilizes the Pearson correlation coefficient analysis of the intrinsic modes including the Residual produced by ICEEMDAN technique and the observed runoff series as provided in Table 1.…”

Section: Decomposition Resultsmentioning

confidence: 99%

“…Reliable and accurate forecasting of runoff and weather are important factors in decision making regarding reservoir management and operation, allocation of water supply, and drought and flood management [3][4][5]. However, devising an efficient model for runoff and rainfall forecasting poses a challenge, since the runoff and rainfall depend on nonlinear factors including precipitation, uneven flow, topography, anthropic activities, and evaporation [6,7]. Owing to its importance, hydrological forecasting is now a popular study area, and researchers have applied many forecasting techniques to predict runoff forecasting in the past decades [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Sibtain¹,

Li²,

Azam³

et al. 2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDAN-GRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.

show abstract

Section: Decomposition Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Sibtain¹,

Li²,

Azam³

et al. 2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…On the other hand, one of the challenging issues is that the models can predict river flow accurately by chance or perform well only in some ranges of input and output variables [65]. To respond to this circumstance, the current research considers two rivers in ULB with different characteristics of input and output variables in order to assess the applicability of the predictive models.…”

Section: Discussionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…All the VMD-based models closely represented the observed flows, and there was no major difference in the performances of the three models. For the two stations, the results show that the prediction results of the VMD-BPNN model were always better than all the other models and required the lowest computational effort [69].…”

Section: Feature Termmentioning

confidence: 94%

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

Zhang

2020

Complexity

View full text Add to dashboard Cite

Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition (VMD) and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. The ensemble forecast was finally reconstructed by summing. Historical daily streamflow series recorded at the Wushan and Weijiabao hydrologic stations from 1 January 2001 to 31 December 2014 in China were investigated using the proposed VMD-based models. Three quantitative evaluation indexes, including the Nash–Sutcliffe efficiency coefficient (NSE), the root mean square error (RMSE), and the mean absolute error (MAE), were used to evaluate and compare the predicted results of the proposed VMD-based models with two other models such as nondecomposition method (BPNN) and BPNN based on ensemble empirical mode decomposition (EEMD-BPNN). Furthermore, a comparative analysis of the performance of the VMD-BPNN model under different forecast periods (1, 3, 5, and 7 days) was performed. The results evidenced that the proposed VMD-based models could always achieve good performance in the testing stage and had relatively good stability and representativeness. Specifically, the VMD-BPNN model considered both the prediction accuracy and computation efficiency. The results show that the reliability of the forecasting decreased as the foresight period increased. The model performed satisfactorily up to 7-d lead time. The VMD-BPNN model could be applied as a promising, reliable, and robust prediction tool for short-term streamflow forecasting modelling.

show abstract

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Cited by 44 publications

References 71 publications

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

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

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

Contact Info

Product

Resources

About