Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle.

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第一作者： Ravinesh C,Deo

第一单位： School of Agricultural, Computational and Environmental Sciences, International Centre for Applied Climate Sciences (ICACS), Institute of Agriculture and Environment (IAg&E), University of Southern Queensland, QLD 4300, Australia. Electronic address: ravinesh.deo@usq.edu.au.

作者： Ravinesh C,Deo ^[1] ; Nathan,Downs ^[2] ; Alfio V,Parisi ^[2] ; Jan F,Adamowski ^[3] ; John M,Quilty ^[3]

作者单位： School of Agricultural, Computational and Environmental Sciences, International Centre for Applied Climate Sciences (ICACS), Institute of Agriculture and Environment (IAg&E), University of Southern Queensland, QLD 4300, Australia. Electronic address: ravinesh.deo@usq.edu.au. ^[1] School of Agricultural, Computational and Environmental Sciences, International Centre for Applied Climate Sciences (ICACS), Institute of Agriculture and Environment (IAg&E), University of Southern Queensland, QLD 4300, Australia. ^[2] Department of Bioresource Engineering, Faculty of Agricultural and Environmental Science, McGill University, Québec H9X 3V9, Canada. ^[3]

关键词 Extreme learning machine M5 Model Tree Multivariate adaptive regression splines Real-time solar forecasting Solar ultraviolet index (UVI)

主题词预测(Forecasting);模型, 理论(Models, Theoretical);昆士兰(Queensland);结果可重复性(Reproducibility of Results);日光(Sunlight);紫外线(Ultraviolet Rays)

DOI 10.1016/j.envres.2017.01.035

PMID 28222363

发布时间 2017-11-19

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