气候变化领域集成服务门户(CCPortal): Application of meemd-arima combining model for annual runoff prediction in the lower Yellow River

CCPortal

DOI	10.2166/wcc.2019.271
	Application of meemd-arima combining model for annual runoff prediction in the lower Yellow River
	Zhang X.; Tuo W.; Song C.
发表日期	2020
ISSN	20402244
起始页码	865
结束页码	876
卷号	11 期号:3
英文摘要	The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudocomponents of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction. © IWA Publishing 2020.
英文关键词	Annual runoff; ARIMA; Lower Yellow River; MEEMD; Prediction
语种	英语
scopus关键词	Flood control; Forecasting; Reservoir management; Reservoirs (water); Rivers; Runoff; Signal processing; Time series; Annual runoff prediction; Auto-regressive integrated moving average; Ensemble empirical mode decompositions (EEMD); Intrinsic Mode functions; Modified ensemble empirical mode decompositions; Non-stationarities; Permutation entropy; Prediction accuracy; Autoregressive moving average model; accuracy assessment; decomposition analysis; ensemble forecasting; hydrological modeling; prediction; runoff; trend analysis; China; Yellow River
来源期刊	Journal of Water and Climate Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/147912
作者单位	School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China; Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou, 450046, China
推荐引用方式 GB/T 7714	Zhang X.,Tuo W.,Song C.. Application of meemd-arima combining model for annual runoff prediction in the lower Yellow River[J],2020,11(3).
APA	Zhang X.,Tuo W.,&Song C..(2020).Application of meemd-arima combining model for annual runoff prediction in the lower Yellow River.Journal of Water and Climate Change,11(3).
MLA	Zhang X.,et al."Application of meemd-arima combining model for annual runoff prediction in the lower Yellow River".Journal of Water and Climate Change 11.3(2020).