Comparison among reconstruction methods for empirical mode decomposition components in runoff prediction
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Abstract:
The accuracy of mid- and long-term forecast on hydrological time series is highly questioned due to the non-stationary and nonlinear complex changes of the series. The "Decomposition - Prediction - Reconstruction" model, as a new and effective forecasting method, has captured the attention of many scholars in related fields in recent years. But troubled by large errors in high-frequency component prediction, uncertain trend and other issues, this model still requires a lot of improvements in the development process. Among all the improvements, reconstruction of runoff component is intensely crucial in controlling high-frequency component prediction error and improving prediction accuracy of the hydrological series. To do this, the "Decomposition - Prediction" hybrid model was established in this paper using empirical mode decomposition (EMD) and autoregressive model (AR). Two reconstruction methods were proposed based on the particle swarm optimization (PSO) algorithm, which were the PSO-based reconstruction coefficient optimization method and the high-frequency component removal & reconstruction coefficient optimization method. These two methods plus the previous high-frequency component removal method were used to compare the efficacy of hydrological forecasting in a case study on Dingjiagou station in northern Shaanxi, Huaxian station in middle Shaanxi and Baihe station in southern Shaanxi. The results showed that the high-frequency component removal method, PSO-based reconstruction coefficient optimization method and high-frequency component removal & reconstruction coefficient optimization method all predict better than the standard reconstruction method, as reflected by five error evaluation indicators. Thus it can be drawn that these three reconstruction methods can improve the prediction accuracy in different degrees. High-frequency component removal method emphasizes removing the high-frequency component, which is the most unstable and unpredictable, so as to enhance the prediction accuracy, but only by a limited margin due to the simple removal process. PSO-based reconstruction coefficient optimization method is to optimize the reconstruction coefficients of all runoff components and to reconstruct the forecasted series. It can reduce errors during components reconstruction to the greatest extent and improve the prediction accuracy effectively. High-frequency component removal & reconstruction coefficient optimization method combines the above two methods and predicts better than all the other methods.
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