Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition

Wang, J.; Wang, Xu; Lei, Xiao Hui; Wang, Hao; Zhang, Xin hua; You, Jinjun; Tan, Qiao feng; Liu, Xiao lian

doi:10.1016/j.jhydrol.2019.124411

Cited by 37 publications

(11 citation statements)

References 59 publications

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“…EEMD optimizes EMD, improving the mode mixing phenomenon of EMD by adding white noise. The EEMD method adds the appropriate amount of white noise to the original sequence and then divides the original sequence into a trend and n finite intrinsic mode functions (IMFs) (Huang and Wu, 2008;Wang et al, 2020). The specific EEMD steps are as follows:…”

Section: Eemdmentioning

confidence: 99%

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Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

et al. 2021

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Hydrological series data are non-stationary and nonlinear. However, certain data-driven forecasting methods assume that streamflow series are stable, which contradicts reality and causes the simulated value to deviate from the observed one. Ensemble empirical mode decomposition (EEMD) was employed in this study to decompose runoff series into several stationary components and a trend. The long short-term memory (LSTM) model was used to build the prediction model for each sub-series. The model input set contained the historical flow series of the simulation station, its upstream hydrological station, and the historical meteorological element series. The final input of the LSTM model was selected by the MI method. To verify the effect of EEMD, this study used the Radial Basis Function (RBF) model to predict the sub-series, which was decomposed by EEMD. In addition, to study the simulation characteristics of the EEMD-LSTM model for different months of runoff, the GM(group by month)-EEMD-LSTM was set up for comparison. The key difference between the GM-EEMD-LSTM model and the EEMD-LSTM model is that the GM model must divide the runoff sequence on a monthly basis, followed by decomposition with EEMD and prediction with the LSTM model. The prediction results of the sub-series obtained by the LSTM and RBF exhibited better statistical performance than those of the original series, especially for the EEMD-LSTM. The overall GM-EEMD-LSTM model performance in low-water months was superior to that of the EEMD-LSTM model, but the simulation effect in the flood season was slightly lower than that of the EEMD-LSTM model. The simulation results of both models are significantly improved compared to those of the LSTM model.

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Section: Eemdmentioning

confidence: 99%

“…Tan et al (2018) used an EEMD-ANN model to forecast the monthly runoff at three stations in Ertan, Cuntan, and Yichang. Wang et al (2020) used ANN and SVR to regress a monthly flow series decomposed by EEMD according to the climate index.…”

Section: Introductionmentioning

confidence: 99%

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

et al. 2021

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“…EEMD can effectively re ect the nature of the original signal and has been widely used in many elds in recent years (An et al 2020). For example, Wang et al (2020) used EEMD to extract the oscillation period and the trend of runoff series and analyzed the relationship between runoff and climate phenomenon indicators. Niu et al (2019) used EEMD to decompose the original monthly ow series, combined the improved gravitational search algorithm (IGSA) and extreme learning machine (ELM) for hydrological prediction, and successfully predicted the monthly runoff of the Three Gorges.…”

Section: Introductionmentioning

confidence: 99%

Ensemble empirical mode decomposition and a long short-term memory neural network for surface water quality prediction of the Xiaofu River, China

Luo

Zhang

Dong

et al. 2022

Preprint

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Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and difficult to apply. However, as water quality time series are generally multiperiod hybrid time series, which have strongly nonlinear and nonstationary characteristics, the prediction accuracy of LSTM for water quality is not high. The ensemble empirical mode decomposition (EEMD) method can decompose the multiperiod hybrid water quality time series into several simpler single-period components. To improve the accuracy of surface water quality prediction, a water quality prediction model based on EEMD-LSTM was proposed in this paper. The water quality time series was first decomposed into several intrinsic mode function components and one residual item, and then these components were used as the input of LSTM to predict water quality. The model was trained and validated using four water quality parameters (NH3N, pH, DO, CODMn) collected from the Xiaofu River and compared with the results of a single LSTM. During the validation period, the R2 values when using LSTM for NH3N, pH, DO and CODMn were 0.567, 0.657, 0.817 and 0.693, respectively, and the R2 values when using EEMD-LSTM for NH3N, pH, DO and CODMn were 0.924, 0.965, 0.961 and 0.936, respectively. The results show that the proposed model outperforms the single LSTM model in various evaluation indicators and greatly improves the model performance in terms of the hysteresis problem. The EEMD-LSTM model has high prediction accuracy and strong generalization ability, and further development may be valuable.

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“…Climate teleconnections, which are widely used in conventional statistical hydrological forecasting (Wang et al, 2020;Steinschneider and Lall, 2016;Mendoza et al, 2017;Lima and Lall, 2010;Mortensen et al, 2018), are an essential part of assessing the skill of GCM forecasts (Neelin and Langenbrunner, 2013). That is, a number of teleconnection patterns are usually investigated upon the issuance of a new set of GCM forecasts (Delworth et al, 2020;Jia et al, 2015;Kim et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach

Zhao

Chen

Shao

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Climate teleconnections are essential for the verification of valuable precipitation forecasts generated by global climate models (GCMs). This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection by using the coefficient of determination (R2). Specifically, observed precipitation is respectively regressed against GCM forecasts, Niño3.4 and both of them, and then the intersection operation is implemented to quantify the overlapping R2 for GCM forecasts and Niño3.4. The significance of overlapping R2 and the sign of ENSO teleconnection facilitate three cases of attribution, i.e., significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection. A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts of global precipitation. For grid cells around the world, the ratio of significantly positive anomaly correlation attributable to positive (negative) ENSO teleconnection is respectively 10.8 % (11.7 %) in December–January–February (DJF), 7.1 % (7.3 %) in March–April–May (MAM), 6.3 % (7.4 %) in June–July–August (JJA) and 7.0 % (14.3 %) in September–October–November (SON). The results not only confirm the prominent contributions of ENSO teleconnection to GCM forecasts, but also present spatial plots of regions where significantly positive anomaly correlation is subject to positive ENSO teleconnection, negative ENSO teleconnection and teleconnections other than ENSO. Overall, the proposed attribution approach can serve as an effective tool to investigate the sources of predictability for GCM seasonal forecasts of global precipitation.

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Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition

Cited by 37 publications

References 59 publications

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

Ensemble empirical mode decomposition and a long short-term memory neural network for surface water quality prediction of the Xiaofu River, China

Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach

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