干旱区生态环境知识资源中心(Arid): Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms

Arid

DOI	10.3390/w15081503
	Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms
	Hou, Wenjie; Yin, Guanghua; Gu, Jian; Ma, Ningning
通讯作者	Yin, GH ; Gu, J
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:8
英文摘要	Accurate estimation of crop evapotranspiration (ETc) is crucial for effective irrigation and water management. To achieve this, support vector regression (SVR) was applied to estimate the daily ETc of spring maize. Random forest (RF) as a data pre-processing technique was utilized to determine the optimal input variables for the SVR model. Particle swarm optimization (PSO) was employed to optimize the SVR model. This study used data obtained from field experiments conducted between 2017 and 2019, including crop coefficient and daily meteorological data. The performance of the innovative hybrid RF-SVR-PSO model was evaluated against a standalone SVR model, a back-propagation neural network (BPNN) model and a RF model, using different input meteorological variables. The ETc values were calculated using the Penman-Monteith equation, which is recommended by the FAO, and used as a reference for the models' estimated values. The results showed that the hybrid RF-SVR-PSO model performed better than all three standalone models for ETc estimation of spring maize. The Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R-2) ranges were 0.956-0.958, 0.275-0.282 mm d(-1), 0.221-0.231 mm d(-1) and 0.957-0.961, respectively. It is proved that the hybrid RF-SVR-PSO model is appropriate for estimation of daily spring maize ETc in semi-arid regions.
英文关键词	spring maize crop evapotranspiration support vector regression particle swarm optimization random forest
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:000978414700001
WOS关键词	SUPPORT VECTOR REGRESSION ; CROP EVAPOTRANSPIRATION ; NETWORK
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399012
推荐引用方式 GB/T 7714	Hou, Wenjie,Yin, Guanghua,Gu, Jian,et al. Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms[J],2023,15(8).
APA	Hou, Wenjie,Yin, Guanghua,Gu, Jian,&Ma, Ningning.(2023).Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms.WATER,15(8).
MLA	Hou, Wenjie,et al."Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms".WATER 15.8(2023).