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Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China

文献类型：期刊论文

作者Dong, Juan4; Zhu, Yuanjun1,3,4; Jia, Xiaoxu2,3; Shao, Ming'an1,2,3; Han, Xiaoyang1; Qiao, Jiangbo1; Bai, Chenyun4; Tang, Xiaodi4
刊名JOURNAL OF HYDROLOGY
出版日期2022
卷号604页码:15
关键词ET0 Convolutional neural network Extreme learning machine Multiple adaptive regression splines
ISSN号0022-1694
DOI10.1016/j.jhydrol.2021.127207
通讯作者Zhu, Yuanjun(zhuyuanjun@foxmail.com)
英文摘要Accurately estimating the reference evapotranspiration (ET0) is a basic requirement for precision irrigation and the correct planning of regional water resources. This study aimed to investigate the spatiotemporal variations in ET0 in China and to improve the accuracy of ET0 calculations on different spatiotemporal scales. Meteorological data collected at 100 stations in China during 1961 to 2019 were used to calculate ET0 with the Penman-Monteith model, and the temporal and spatial patterns in ET0-PM were analyzed with the Mann-Kendall nonparametric trend test method. Three machine learning models comprising convolutional neural network (CNN), extreme learning machine (ELM), and multiple adaptive regression splines (MARS), and seven empirical models calibrated with mind evolutionary algorithm (MEA) were compared to assess their suitability for calculating ET0 on different spatiotemporal scales in China. The results showed that the annual mean ET0-PM value (413.29-2772.35 mm) in China gradually increased from north to south and from west to east. ET0 exhibited an upward trend in the temperate continental zone (TCZ) and mountain plateau zone (MPZ) but a downward trend in the temperate monsoon zone (TMZ) and subtropical monsoon region (SMZ). By comparing the global performance indicators (GPI), the machine learning models generally performed better than the empirical models at different spatiotemporal scales. And CNN was the best model for calculating ET0 in terms of the model accuracy and stability. On the daily scale, MARS performed well in MPZ, whereas ELM performed well in TMZ and TCZ. On the monthly scale, MARS performed well in TMZ, whereas ELM performed well in SMZ and MPZ. At the annual scale, the accuracy of ELM was higher than that of MARS.
WOS关键词LIMITED METEOROLOGICAL DATA ; POTENTIAL EVAPOTRANSPIRATION ; CLIMATE-CHANGE ; SVM ; EVAPORATION ; REGRESSION ; REGION ; ELM
资助项目Strategy Priority Research Program of Chinese Academy of Sciences[XDB40000000] ; Natural Sci-ence Foundation of China[41530854] ; Natural Sci-ence Foundation of China[42007011]
WOS研究方向Engineering ; Geology ; Water Resources
语种英语
WOS记录号WOS:000731346400003
出版者ELSEVIER
资助机构Strategy Priority Research Program of Chinese Academy of Sciences ; Natural Sci-ence Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/168723]  
专题中国科学院地理科学与资源研究所
通讯作者Zhu, Yuanjun
作者单位1.Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China
4.Northwest A&F Univ, Inst Soil & Water Conservat, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Dong, Juan,Zhu, Yuanjun,Jia, Xiaoxu,et al. Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China[J]. JOURNAL OF HYDROLOGY,2022,604:15.
APA Dong, Juan.,Zhu, Yuanjun.,Jia, Xiaoxu.,Shao, Ming'an.,Han, Xiaoyang.,...&Tang, Xiaodi.(2022).Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China.JOURNAL OF HYDROLOGY,604,15.
MLA Dong, Juan,et al."Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China".JOURNAL OF HYDROLOGY 604(2022):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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