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Water 2018, 10(7), 853; https://doi.org/10.3390/w10070853

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

1
College of Water Resources & Civil Engineering, China Agricultural University, Beijing100083, China
2
State Key Laboratory of Hydroscience & Engineering, Tsinghua University, Beijing 100084, China
*
Authors to whom correspondence should be addressed.
Received: 18 May 2018 / Revised: 8 June 2018 / Accepted: 20 June 2018 / Published: 27 June 2018
(This article belongs to the Section Water Resources Management and Governance)
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Abstract

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. View Full-Text
Keywords: long-term streamflow forecasting; empirical mode decomposition (EMD); ensemble empirical mode decomposition (EEMD); Autoregressive Integrated Moving Average (ARIMA); hybrid model long-term streamflow forecasting; empirical mode decomposition (EMD); ensemble empirical mode decomposition (EEMD); Autoregressive Integrated Moving Average (ARIMA); hybrid model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, Z.-Y.; Qiu, J.; Li, F.-F. Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting. Water 2018, 10, 853.

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