干旱区生态环境知识资源中心(Arid): Estimating potential evapotranspiration based on self-optimizing nearest neighbor algorithms: a case study in arid-semiarid environments, Northwest of China

Arid

DOI	10.1007/s11356-019-06597-7
	Estimating potential evapotranspiration based on self-optimizing nearest neighbor algorithms: a case study in arid-semiarid environments, Northwest of China
	Feng, Kepeng 1,2,3; Tian, Juncang 1,2,3
通讯作者	Feng, Kepeng
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2020
卷号	27 期号:30 页码:37176-37187
英文摘要	Changes in potential evapotranspiration will affect the surface ecology and environment of the land. Accurate and quick estimation of potential evapotranspiration will help to analyze environmental change. In this study, in combination with the canonical correlation analysis (CCA) and k-nearest neighbor algorithm (k-NN), a new method for calculating potential evapotranspiration (CCA-k-NN) based on self-optimizing nearest neighbor algorithm was proposed, in which less meteorological data were used for estimation. By analyzing the basic principles of CCA and k-NN and according to the requirement of estimating ET0, the CCA-k-NN method was constructed, and its basic principles and key steps were described. In this method, CCA algorithm was used to find the most relevant meteorological data for potential evapotranspiration, and the dimensionality of meteorological data for subsequent estimation of ET0 was reduced. Then, k-NN algorithm was used to estimate ET0. The Northwest of China was chosen as the research area to evaluate the applicability of this method. The 148 data stations in the region were divided into training datasets, testing datasets, and validation datasets. ET0 was estimated on three datasets using the proposed method, and the estimation accuracy of the CCA-k-NN method was evaluated with FAO-56 Penman-Monteith as a reference. The results show that the CCA-k-NN method maintains a high correlation with FAO-56 Penman-Monteith (correlation coefficient is greater than 0.9) and has a good estimation accuracy. RMSE and MAE are both less than 1 mm day(-1), and the overall performance of NSCE is greater than 0.5, all of which reach the level of applicable and above. At the same time, the CCA-k-NN method has low time complexity O(n). Comparison of the results of the CCA-k-NN method with those of other empirical models showed that the CCA-k-NN method is more accurate and can be employed successfully in estimating ET0.
英文关键词	Potential evapotranspiration Canonical correlation analysis k-Nearest neighbor algorithm Limited meteorological data Northwest China Arid semiarid environments
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000492369500001
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; PAN EVAPORATION ; MACHINE ; MODELS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
EI主题词	2019-10-25
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310357
作者单位	1.Ningxia Univ, Sch Civil & Hydraul Engn, 539 Helan Mt Rd, Yinchuan 750021, Ningxia, Peoples R China; 2.Engn Res Ctr Efficient Utilizat Water Resources M, Yinchuan, Peoples R China; 3.Ningxia Res Ctr Technol Water Saving Irrigat & Wa, Yinchuan, Peoples R China
推荐引用方式 GB/T 7714	Feng, Kepeng,Tian, Juncang. Estimating potential evapotranspiration based on self-optimizing nearest neighbor algorithms: a case study in arid-semiarid environments, Northwest of China[J],2020,27(30):37176-37187.
APA	Feng, Kepeng,&Tian, Juncang.(2020).Estimating potential evapotranspiration based on self-optimizing nearest neighbor algorithms: a case study in arid-semiarid environments, Northwest of China.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,27(30),37176-37187.
MLA	Feng, Kepeng,et al."Estimating potential evapotranspiration based on self-optimizing nearest neighbor algorithms: a case study in arid-semiarid environments, Northwest of China".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 27.30(2020):37176-37187.