干旱区生态环境知识资源中心(Arid): Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

Arid

DOI	10.1016/j.compag.2021.106211
	Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China
	Dong, Jianhua; Liu, Xiaogang; Huang, Guomin; Fan, Junliang; Wu, Lifeng; Wu, Jie
通讯作者	Wu, LF (corresponding author), Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China.
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2021
卷号	186
英文摘要	Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET0 across China. Monthly meteorological data [including maximum air temperature (Tmax), minimum air temperature (Tmin), extra-terrestrial solar radiation (Ra), relative humidity (RH), global solar radiation (Rs), wind speed (U)] during 1966-2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R2 = 0.9814, RMSE = 0.2143 mm d-1). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, Rs was greater than Ra, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7%, respectively), indicating a problem of overfitting. Conversely, the GWO-KNEA model was the most stable model with the smallest increase in RMSE during the testing phase (4.4-15.7%) among all models. In summary, taking the model's estimation accuracy, stability, convergence rate, and climate environment into account, our results suggest that GWO-KNEA would be a suitable model for estimating ET0 in different climate zones of China and the most practical input combination would be combination 2.
英文关键词	Reference evapotranspiration Climatic zones Grasshopper optimization algorithm Grey wolf optimization algorithm Machine learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000670100500002
WOS关键词	SUPPORT-VECTOR-MACHINE ; GLOBAL SOLAR-RADIATION ; ARPS DECLINE MODEL ; LIMITED DATA ; NEURO-FUZZY ; SENSITIVITY ; SIMULATION ; TRENDS ; MAIZE ; FIELD
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
来源机构	西北农林科技大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368651
作者单位	[Dong, Jianhua; Huang, Guomin; Wu, Lifeng] Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China; [Liu, Xiaogang] Kunming Univ Sci & Technol, Fac Agr & Food, Kunming 650500, Yunnan, Peoples R China; [Fan, Junliang] Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid & Semiarid Are, Minist Educ, Yangling 712100, Shaanxi, Peoples R China; [Wu, Jie] Wuhan Polytech Univ, Sch Civil Engn & Architecture, Wuhan 430023, Peoples R China
推荐引用方式 GB/T 7714	Dong, Jianhua,Liu, Xiaogang,Huang, Guomin,et al. Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China[J]. 西北农林科技大学,2021,186.
APA	Dong, Jianhua,Liu, Xiaogang,Huang, Guomin,Fan, Junliang,Wu, Lifeng,&Wu, Jie.(2021).Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China.COMPUTERS AND ELECTRONICS IN AGRICULTURE,186.
MLA	Dong, Jianhua,et al."Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China".COMPUTERS AND ELECTRONICS IN AGRICULTURE 186(2021).