干旱区生态环境知识资源中心(Arid): Prediction Model for Daily Reference Crop Evapotranspiration Based on Hybrid Algorithm in Semi-Arid Regions of China

Arid

DOI	10.3390/atmos13060922
	Prediction Model for Daily Reference Crop Evapotranspiration Based on Hybrid Algorithm in Semi-Arid Regions of China
	Zhao, Xinbo; Li, Yuanze; Zhao, Zhenhua; Xing, Xuguang; Feng, Guohua; Bai, Jiayi; Wang, Yuhang; Qiu, Zhaomei; Zhang, Jing
通讯作者	Qiu, ZM
来源期刊	ATMOSPHERE
EISSN	2073-4433
出版年	2022
卷号	13 期号:6
英文摘要	The accurate estimation of reference crop evapotranspiration (ETO) plays an important role in guiding regional water resource management and crop water content research. In order to improve the accuracy of ETO prediction in regions with missing data, this study used the partial correlation analysis method to select factors that have a large impact on ETO as input combinations to construct ETO estimation models for typical stations in semi-arid regions of China. A biological heuristic optimization algorithm (Golden Eagle optimization algorithm (GEO) and Sparrow optimization algorithm (SSA)) and Extreme Learning Machine model (ELM) were combined to improve the estimation accuracy. The results showed that Ra was the primary factor affecting the ETO model, with an importance range of 0.187-0.566. Compared with the independent ELM model, the hybrid model has higher accuracy and stability. The estimated value of the SSA-ELM model under five-factor input condition (Ra, RH, T-max, T-min, U-2) is closest to the standard value calculated by FAO56 PM: RMSE = 0.067-0.085, R-2 = 0.998-0.999, MAE = 0.050-0.066 and NSE = 0.998-0.999. In general, the combination of a partial correlation analysis algorithm and a hybrid model can be used to estimate ETO with high accuracy under the condition of reducing input factors. Use of the first five factors extracted from the partial correlation analysis algorithm as input to build an ETO estimation model based on SSA-ELM in China's semi-arid regions is recommended, which can also provide a reference for ETO estimation in similar regions.
英文关键词	reference crop evapotranspiration modeling optimization key factors China's semi-arid regions
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000816565800001
WOS关键词	PENMAN-MONTEITH REFERENCE ; EXTREME LEARNING-MACHINE ; SUPPORT VECTOR MACHINE ; NEURAL-NETWORK ; NET-RADIATION ; EQUATIONS ; COMPONENTS ; REGRESSION ; VARIABLES ; SVM
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391914
推荐引用方式 GB/T 7714	Zhao, Xinbo,Li, Yuanze,Zhao, Zhenhua,et al. Prediction Model for Daily Reference Crop Evapotranspiration Based on Hybrid Algorithm in Semi-Arid Regions of China[J],2022,13(6).
APA	Zhao, Xinbo.,Li, Yuanze.,Zhao, Zhenhua.,Xing, Xuguang.,Feng, Guohua.,...&Zhang, Jing.(2022).Prediction Model for Daily Reference Crop Evapotranspiration Based on Hybrid Algorithm in Semi-Arid Regions of China.ATMOSPHERE,13(6).
MLA	Zhao, Xinbo,et al."Prediction Model for Daily Reference Crop Evapotranspiration Based on Hybrid Algorithm in Semi-Arid Regions of China".ATMOSPHERE 13.6(2022).