干旱区生态环境知识资源中心(Arid): Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

Arid

DOI	10.2166/nh.2020.012
	Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region
	Wu, Min; Feng, Qi; Wen, Xiaohu; Deo, Ravinesh C.; Yin, Zhenliang; Yang, Linshan; Sheng, Danrui
通讯作者	Wen, XH
来源期刊	HYDROLOGY RESEARCH
ISSN	0029-1277
EISSN	2224-7955
出版年	2020
卷号	51 期号:4 页码:648-665
英文摘要	The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET(0)is estimated by an appropriate combination of model inputs comprising maximum air temperature (T-max), minimum air temperature (T-min), sunshine durations (S-un), wind speed (U-2), and relative humidity (R-h). The output of RF models are tested by ET(0)calculated using Penman-Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET(0)for the arid oasis area with limited data. Besides,R(h)was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET(0)in the arid areas where reliable weather data sets are available, but relatively limited.
英文关键词	arid areas evapotranspiration Monte Carlo predict random forest
类型	Article
语种	英语
开放获取类型	DOAJ Gold, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000565303800005
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; SUPPORT-VECTOR-MACHINE ; LIMITED CLIMATIC DATA ; WEATHER PARAMETERS ; REGRESSION ; CLASSIFICATION ; SVM
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325979
作者单位	[Wu, Min; Feng, Qi; Wen, Xiaohu; Yin, Zhenliang; Yang, Linshan; Sheng, Danrui] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Lanzhou 730000, Peoples R China; [Wu, Min; Sheng, Danrui] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Deo, Ravinesh C.] Univ Southern Queensland, Sch Agr Computat & Environm Sci, Ctr Sustainable Agr Syst, Springfield, Qld 4300, Australia
推荐引用方式 GB/T 7714	Wu, Min,Feng, Qi,Wen, Xiaohu,et al. Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region[J],2020,51(4):648-665.
APA	Wu, Min.,Feng, Qi.,Wen, Xiaohu.,Deo, Ravinesh C..,Yin, Zhenliang.,...&Sheng, Danrui.(2020).Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region.HYDROLOGY RESEARCH,51(4),648-665.
MLA	Wu, Min,et al."Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region".HYDROLOGY RESEARCH 51.4(2020):648-665.