基于贝叶斯模型加权平均法的径流序列高频分量预测研究

Application of Bayesian model averaging method to prediction of high-frequency components in runoff series

"分解–预测–重构"模式作为一种新的预测思路,已被广泛用于非平稳径流序列的中长期预测。但由于分解之后高频分量预测精度较差,致使该模式的预测效果并不理想。本文采用径向基神经网络(RBF)、自回归模型(AR)以及均生函数模型(MGF)分别对陕北无定河丁家沟站实测径流由经验模态分解(EMD)得到的高频分量进行预测,利用贝叶斯模型加权平均法(BMA)对其集成,着重分析比较了基于BMA的集成方法和单一模型的预测效果,验证了BMA方法在处理高频分量误差控制方面的可行性。结果显示基于BMA的高频分量预测的相对误差绝对平均值较单一模型有所降低,径流预测整体精度有显著提升。这表明BMA集成方法能够有效地降低径流序列中高频分量的预测误差,提高整体预测精度,可作为一种有效的方法,供其他类似非平稳预测问题所借鉴。

River streamflow has gradually developed into a non-stationary and non-linear complex process under the influences of climate change and human interferences. A major technical issue associated with this environmental changing is how to predict accurately the future change in river runoff. At present, a new prediction system, namely decomposition-prediction-reconstruction, has been widely used in the mid-and long-term prediction of runoff series. Its prediction efficiency, however, is unsatisfactory due to large errors in its prediction of high-frequency components that are decomposed using the empirical mode decomposition（EMD）. To forecast the high-frequency components in the runoff at the Dingjiagou gauge station on the Wuding River, this study has adopted three approaches： the radial basis function（RBF） neural network, autoregressive（AR） model, and mean generating function（MGF） model. Based on these models, a comprehensive prediction was also made using the Bayesian model averaging（BMA） method. In this paper, we confirm the accuracy of BMA and demonstrate its effective control on the prediction error of high-frequency components through a comparison of its errors with those of the three single models. Thus, this study comes to a conclusion that the BMA method is an effective approach to improve the prediction accuracy of runoff series and would provide valuable references for similar issues in forecasting non-stationary time series.