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Annual runoff forecast based on a combined EEMD-ARIMA model
In order to improve the accuracy of hydrological models for runoff prediction and to solve the problem of disappearing time series data break or data fluctuation due to the extension of time series, monthly runoff data from 1996 to 2015 at the Cuntan hydrological station in the upper reaches of the Yangtze River are used as the basis to develop the annual runoff prediction. Prediction of the five Intrinsic Mode Function (IMF) components and one residual from the Ensemble Empirical Mode Decomposition (EEMD) decomposition used different models of Auto-Regressive Integrated Moving Average Model (ARIMA). Except for the small errors in the first two components, the rest of the ARIMA models are highly fitted. For prediction of runoff in 2014 and 2015 based on EEMD-ARIMA model, the relative errors are 6.26% and −1.17%. Compared with the single ARIMA model and Back Propagation (BP) model, the prediction effect is better, and the prediction method is simple and clear. It is shown that the combined EEMD-ARIMA model is an efficient and useful method for the prediction of annual runoff volume. HIGHLIGHTS The study used 240 months of runoff data from Cuntan hydrological station in the upper reaches of the Yangtze River.; An annual runoff prediction model based on EEMD-ARIMA hybrid method is proposed.; The EEMD-ARIMA model outperforms the EMD-ARIMA, BP, and LSTM models.;
Annual runoff forecast based on a combined EEMD-ARIMA model
In order to improve the accuracy of hydrological models for runoff prediction and to solve the problem of disappearing time series data break or data fluctuation due to the extension of time series, monthly runoff data from 1996 to 2015 at the Cuntan hydrological station in the upper reaches of the Yangtze River are used as the basis to develop the annual runoff prediction. Prediction of the five Intrinsic Mode Function (IMF) components and one residual from the Ensemble Empirical Mode Decomposition (EEMD) decomposition used different models of Auto-Regressive Integrated Moving Average Model (ARIMA). Except for the small errors in the first two components, the rest of the ARIMA models are highly fitted. For prediction of runoff in 2014 and 2015 based on EEMD-ARIMA model, the relative errors are 6.26% and −1.17%. Compared with the single ARIMA model and Back Propagation (BP) model, the prediction effect is better, and the prediction method is simple and clear. It is shown that the combined EEMD-ARIMA model is an efficient and useful method for the prediction of annual runoff volume. HIGHLIGHTS The study used 240 months of runoff data from Cuntan hydrological station in the upper reaches of the Yangtze River.; An annual runoff prediction model based on EEMD-ARIMA hybrid method is proposed.; The EEMD-ARIMA model outperforms the EMD-ARIMA, BP, and LSTM models.;
Annual runoff forecast based on a combined EEMD-ARIMA model
Xianqi Zhang (author) / Yaohui Lu (author) / Guoyu Zhu (author) / Xilong Wu (author) / Dong Zhao (author) / Bingsen Duan (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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