Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting

Zhao, Xuehua; Chen, Xu

doi:10.1007/s11269-015-0977-z

Cited by 63 publications

(23 citation statements)

References 33 publications

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“…Auto-regressive (AR) error correction model is a kind of time series models which search the relationship of relevant factors to forecast target component only through its own historical observations (Zhang et al 2011;Zhao and Chen 2015). Since the effects of totally same data fitted by different order numbers of AR models are different, we choose Akaike Information Criterion (AIC) index to select the best order of AR model to fit object series (Symonds and Moussalli 2011).…”

Section: Real-time Correction Methodsmentioning

confidence: 99%

Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting

Zhou

Chen

et al. 2015

Water Resour Manage

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With much more higher requirement for the precision of flood forecasting and length of forecast period in the hydrological operational predication, three coupling forecast methods which include real-time correction-combination forecast (RC-CF) method, combination forecast-realtime correction (CF-RC) method, and Integral Parameters Optimization (IPO) method are proposed in this paper for the purpose of improving the precision of flood forecasting. These coupling forecast methods are based on the real-time correction and combination forecast methods. Thereafter, two methods (method A & method B) are proposed for the purpose of prolonging the forecast period. Furthermore, indices of accuracy assessment which consist of mean absolute error, mean relative error, certainty coefficient and root-mean-square error are utilized to evaluate the forecast results of coupling forecast methods. Moreover, with a case study of Xiangjiaba station in the Jinsha River, advantages and disadvantages of these coupling forecast methods are obtained through the comparison of forecast results calculated by these methods, and they provide the basis for selection of coupling forecast methods. The result shows that the IPO method performs better than other two methods which behave undifferentiated. It is found that the IPO method combined with method B can be a viable alternative for flood forecasting of multiple hydrological models.

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Section: Real-time Correction Methodsmentioning

confidence: 99%

Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting

Zhou

Chen

et al. 2015

Water Resour Manage

View full text Add to dashboard Cite

show abstract

“…Kisi et al [45] developed EMD-ANN hybrid models with a predictive accuracy correlation coefficient (R) of 0.801. Attempts at coupling EEMD with ANN were also carried out for hydrological forecasting [14,46,47]; the original hydrological time series was decomposed into several components by EEMD and these components were taken as inputs of ANN models for forecasting, and the results verified its validity.…”

Section: Introductionmentioning

confidence: 97%

Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting

Wang

Qiu

2018

Water

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Long-term streamflow forecast is of great significance for water resource application and management. However, accurate monthly streamflow forecasting is challenging due to its non-stationarity and uncertainty. Time series analysis methods have been proved to perform well in stationary time series forecasting, which can be derived from decomposition of the non-stationary sequence. As common decomposition methods in time domain, Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are selected to decompose the components with different time-scale characteristics in the original hydrological time series in this study. The derived components are proved to be stationary by the stationarity test. Thus, Autoregressive Integrated Moving Average (ARIMA) model, a simple and effective time series analysis method, is used to forecast the components. A hybrid EMD/EEMD-ARIMA model is proposed in this study for long-term streamflow forecasting, which is applied to the upper stream of the Yellow River. The original daily streamflow time series of six years at the Tangnaihai station are firstly decomposed by EMD/EEMD into several stationary or simple non-stationary sub-series to explore detailed data information with different time scales. ARIMA models with appropriate parameters are then established for each subsequence to forecast the stream flow of the next year. Predicted ten-day and monthly stream flow is finally obtained combing the predictions of all the components. The EMD-ARIMA hybrid model performs best in forecasting high and moderate value of streamflow and fits best with the observation compared with EEMD-ARIMA and ARIMA models. The results not only verify the effectiveness of the proposed hybrid EMD/EEMD-ARIMA model in exploiting comprehensive information to improve the prediction but also indicate that the EMD-ARIMA model with end points disposal performs the best and can be used for long-term hydrological forecasting.

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“…Therefore, traditional (i.e., linear interpretation) methods cannot satisfy for capturing nonlinear, nonstationary response patterns of streamflow to climatic variability. Wu and Huang [14] developed a time-series signal processing method, ensemble empirical mode decomposition (EEMD), that has a stronger self-adaptability and local variation characteristics based on the signal [13,15,16]. The method can gradually separate the oscillations at different scales in hydroclimatic change research (Intrinsic Mode Function-IMF) and the trend components from the original signal.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Response of Streamflow to Climate Change in High-Latitude Regions: A Case Study in Headwaters of Nenjiang River Basin in China’s Far Northeast

Zhang

Shen

et al. 2018

Water

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Assessment of the response of streamflow to future climate change in headwater areas is of a particular importance for sustainable water resources management in a large river basin. In this study, we investigated multiscale variation in hydroclimatic variables including streamflow, temperature, precipitation, and evapotranspiration in the Headwater Areas of the Nenjiang River Basin (HANR) in China's far northeast, which are sensitive to climate change. We analyzed 50-year-long records of the hydroclimatic variables using the ensemble empirical mode decomposition (EEMD) method to identify their inherent changing patterns and trends at the inter-annual and inter-decadal scales. We found that all these hydroclimatic variables showed a clear nonlinear process. At the inter-annual and inter-decadal scales, streamflow had a similar periodic changing pattern and transition years to that of precipitation; however, within a period, streamflow showed a close association with temperature and evapotranspiration. The findings indicate that the response of streamflow in headwater regions to climate change is a nonlinear dynamic process dictated by precipitation at the decadal scale and modified by temperature and evapotranspiration within a decade.

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Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting

Cited by 63 publications

References 33 publications

Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting

Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting

Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting

Nonlinear Response of Streamflow to Climate Change in High-Latitude Regions: A Case Study in Headwaters of Nenjiang River Basin in China’s Far Northeast

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