摘要
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.
作者
李继清
王爽
吴月秋
田雨
LI Jiqing;WANG Shuang;WU Yueqiu;TIAN Yu(School of Water Resources and Hydropower Engineering,North China Electric Power University,Beijing 102206;China Institute of Water Resources and Hydropower Research,Beijing 100038)
出处
《水力发电学报》
CSCD
北大核心
2021年第7期13-22,共10页
Journal of Hydroelectric Engineering
基金
国家重点研发计划(2016YFC0402208,2017YFC0405906)
国家自然科学基金项目(51879273)。
关键词
极点对称模态分解
ELMAN神经网络
时间尺度
径流预报
非平稳序列
长江上游
extreme-point symmetric mode decomposition
Elman neural network
time scale
runoff forecast
non-stationary series
upper reaches of Yangtze Rive
