径流预报的极点对称模态分解-Elman网络模型

Runoff forecasts using combined model of extreme-point symmetric mode decomposition and Elman neural network

针对径流序列非线性、非平稳的特点,将极点对称模态分解(ESMD)方法与Elman神经网络模型相结合,建立了ESMD-Elman神经网络组合模型,并应用于长江上游干支流8站的年、月径流预报。首先利用ESMD方法将径流序列分解为各模态分量和趋势余项;然后利用Elman神经网络模型分别预测各平稳序列;最后加和重构得到最终预测结果。结果表明:组合模型预报精度大于单一模型,与ESMD-BP神经网络组合模型比,ESMDElman神经网络组合模型的8站年径流预报结果的平均相对误差(MAPE)平均降低3.6%,均方根误差(RMSE)平均降低7.8%,确定性系数平均提高5.0%;8站月径流预报结果的MAPE平均降低3.0%,RMSE平均降低2.8%,具有"分解→预测→重构"特点的组合模型提高了预报精度。

Aiming at the nonlinear and non-stationary characteristics of runoff sequences,we develop a combined model of extreme-point symmetric mode decomposition(ESMD)and Elman neural network,and apply it to annual and monthly runoff forecasts at eight stations in the upper reaches of the Yangtze River.First,ESMD is used to decompose a runoff sequence into modal components and trend remainders;then,the Elman neural network model is used to predict each of the stationary sequences;lastly,final prediction results are obtained by adding and reconstruction.The results show this combined model has forecast accuracy higher than that of a single model.Compared with the ESMD-BP neural network combination model,for annual runoff forecasts,it has an average reduction of 3.6%in mean absolute percentage error(MAPE)and 7.8%in root mean square error(RMSE),and an average increase of 5.0%in determination coefficient for the eight stations;while for monthly runoff forecasts,the MAPE is decreased by an average of 3.0%and the RMSE decreased by an average of 2.8%.Our combined model,characterized by decomposition-prediction-reconstruction,improves prediction accuracy.