EEMD-NNBR模型在降水预测中的应用  

Application of EEMD-NNBR Model in Rainfall Forecasting

作　　者：姚欣明 陈元芳[1] 顾圣华 黄琴[1] 康有[1] 

机构地区：[1]河海大学水文水资源学院,江苏南京210098 [2]上海市水文总站,上海200232

出　　处：《水电能源科学》2014年第12期11-13,16,共4页Water Resources and Power

摘　　要：为解决降水资源预测复杂的问题,建立了具有物理意义的新预测模型,即利用集合经验模态分解(EEMD)方法,分解降水资源并识别其演变模式,获得各本征模函数(IMF),然后结合最近邻抽样回归模型(NNBR)对数据进行预测分析,汇总相应的计算结果,从而构成了EEMD-NNBR降水预测模型。以无锡市惠山区的降水序列资料为例,采用EEMD-NNBR模型预测降水资源,并与单一的NNBR模型预测值进行对比分析。结果表明,所建模型稳定性较好,能合理预测水资源演变趋势,提高降水资源预测精度,具有一定的应用价值。For solving complicated rainfall prediction, this paper proposes a new physical model. Ensemble empiricalmodel decomposition （EEMD） is used to decompose rainfall series and identify its evolution mode. And then each inher-ent components function （IMF） is obtained. Meanwhile, an analysis of each IMF has been made by nearest neighborbootstrapping regressive （NNBR） model. Finally, the prediction of each IMF is aggregated as the final result of EEMD-NNBR model. Taking Huishan district in Wuxi City for an example, the annual rainfall is predicted by the EEMDNNBRmethod and the results are compared with those of single NNBR model. The results show that the model has better sta-bility and it can predict evolution trend of water resources as well as improve prediction accuracy, which has a certain ap-plication value.

关 键 词：EEMD NNBR EEMD-NNBR模型 降水 预测 

分 类 号：TV124[水利工程—水文学及水资源]