气候变化领域集成服务门户(CCPortal): Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area

CCPortal

DOI	10.1016/j.agwat.2023.108161
	Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area
	He, Chaofei; Chen, Fulong; Long, Aihua; Qian, YuXia; Tang, Hao
发表日期	2023
ISSN	0378-3774
EISSN	1873-2283
卷号	279
英文摘要	The optimized agricultural water schedule and management, especially in the large irrigation district along the river basins, were closely related to the spatial and temporal runoff variations. However, the impacts of climate change and human activities leads to non-linear and non-stationary monthly runoffs. Under this condition, effectively capturing the variation of the runoff time series and improving the accuracy of the prediction model are of vital importance. However, some existing monthly data-driven prediction studies mainly focus on the model structure and calculation load, ignoring the influence of each frequency component of runoff and the fluctuation of runoff series caused by meteorological factors, which may not effectively capture the potential change process. In this paper, a novel method (CEEMD-MPE-EMD-GRU) for the non-stationary monthly runoff prediction was proposed. It fully took advantages of the complementary ensemble empirical mode decomposition (CEEMD), multi-scale permutation entropy (MPE), empirical mode decomposition (EMD) and gated recurrent unit (GRU). The combined model in general is a data-driven model, and compared with the traditional mech-anism model, its most notable advantage is that it successfully overcomes the redundant information of the prediction model. In addition, atmospheric input factor analysis is added on the basis of fully decomposing and identifying non-stationary pseudo-components. The hydrological runoff data (1956-2014) obtained from the Manas River locating at Xinjiang, China were used for prediction. The results indicated that the new CEEMD-MPE-EMD-GRU model reached higher accuracy, as its Nash-Sutcliffe efficiency coefficient (0.960) was signifi-cantly larger than those of the GRU model (0.813) and the CEEMD-GRU model (0.889). Meanwhile, the root mean square error and the absolute relative error of the CEEMD-MPE-EMD-GRU model decreased to 0.279 and 0.195, respectively. The new runoff prediction model established in this paper would provide more precise evaluation of the monthly runoff prediction and better guidelines for high-efficiency agricultural water sched-uling in the irrigation district.
英文关键词	B; E; Clothier; Monthly runoff prediction; Hybrid model; Model precision; Oasis irrigation area
语种	英语
WOS研究方向	Agronomy ; Water Resources
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000930752000001
来源期刊	AGRICULTURAL WATER MANAGEMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281314
作者单位	Shihezi University; China Institute of Water Resources & Hydropower Research
推荐引用方式 GB/T 7714	He, Chaofei,Chen, Fulong,Long, Aihua,et al. Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area[J],2023,279.
APA	He, Chaofei,Chen, Fulong,Long, Aihua,Qian, YuXia,&Tang, Hao.(2023).Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area.AGRICULTURAL WATER MANAGEMENT,279.
MLA	He, Chaofei,et al."Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area".AGRICULTURAL WATER MANAGEMENT 279(2023).