干旱区生态环境知识资源中心(Arid): Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data

Arid

DOI	10.1016/j.compag.2020.105430
	Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data
	Zhu, Bin 1,2; Feng, Yu 1,2,3; Gong, Daozhi 3; Jiang, Shouzheng 1,2; Zhao, Lu 1,2; Cui, Ningbo 1,2
通讯作者	Cui, Ningbo
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2020
卷号	173
英文摘要	Accurate prediction of reference evapotranspiration (ETo) is pivotal to the determination of crop water requirement and irrigation scheduling in agriculture as well as water resources management in hydrology. In the present study, the particle swarm optimization (PSO) algorithm was utilized to optimally determine the parameters of the extreme learning machine (ELM) model, and a novel hybrid PSO-ELM model was thus proposed for estimating daily ETo in the arid region of Northwest China with limited input data. The PSO-ELM model was compared with the original ELM, artificial neural networks (ANN) and random forests (RF) models as along with six empirical models (including radiation-, temperature- and mass transfer-based empirical models). Three input combinations were utilized to develop the data-driven models, which corresponded to the radiation-, temperature- and mass transfer-based models, respectively. The results indicated that machine learning models provided more accurate ETo estimates, compared with the corresponding empirical models with the same inputs. The hybrid PSO-ELM model exhibited better performance than the other models for daily ETo estimation as indicated by the statistical results. Although radiation-based machine learning models outperformed temperature- and mass transfer-based machine learning models, the temperature-based PSO-ELM model obtained reasonable results when only air temperature data were available, which was considered as a promising model for forecasting future ETo with temperature data. Overall, the PSO-ELM model was superior to the other machine learning and empirical models, which was thus recommended to predict daily ETo with limited inputs in the arid region of Northwest China.
英文关键词	Particle swarm optimization Extreme learning machine Random forests Reference evapotranspiration Modeling
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000531080400046
WOS关键词	MODELING REFERENCE EVAPOTRANSPIRATION ; ARTIFICIAL NEURAL-NETWORK ; REFERENCE CROP EVAPOTRANSPIRATION ; GLOBAL SOLAR-RADIATION ; PAN EVAPORATION ; METEOROLOGICAL DATA ; ARID REGIONS ; TEMPERATURE ; EQUATIONS ; REGRESSION
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319259
作者单位	1.Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Chengdu, Peoples R China; 2.Sichuan Univ, Coll Water Resource & Hydropower, Chengdu, Peoples R China; 3.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Bin,Feng, Yu,Gong, Daozhi,et al. Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data[J],2020,173.
APA	Zhu, Bin,Feng, Yu,Gong, Daozhi,Jiang, Shouzheng,Zhao, Lu,&Cui, Ningbo.(2020).Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data.COMPUTERS AND ELECTRONICS IN AGRICULTURE,173.
MLA	Zhu, Bin,et al."Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data".COMPUTERS AND ELECTRONICS IN AGRICULTURE 173(2020).