DOI: 10.1007/s00382-016-3145-0
Scopus记录号: 2-s2.0-84965038762
论文题名: Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques
作者: Sarhadi A. ; Burn D.H. ; Yang G. ; Ghodsi A.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2017
卷: 48, 期: 2017-03-04 起始页码: 1329
结束页码: 1351
语种: 英语
英文关键词: Climate change
; Dimensionality reduction
; Statistical downscaling
; Supervised learning
; Supervised Principal Component Analysis (S-PCA)
英文摘要: One of the main challenges in climate change studies is accurate projection of the global warming impacts on the probabilistic behaviour of hydro-climate processes. Due to the complexity of climate-associated processes, identification of predictor variables from high dimensional atmospheric variables is considered a key factor for improvement of climate change projections in statistical downscaling approaches. For this purpose, the present paper adopts a new approach of supervised dimensionality reduction, which is called “Supervised Principal Component Analysis (Supervised PCA)” to regression-based statistical downscaling. This method is a generalization of PCA, extracting a sequence of principal components of atmospheric variables, which have maximal dependence on the response hydro-climate variable. To capture the nonlinear variability between hydro-climatic response variables and projectors, a kernelized version of Supervised PCA is also applied for nonlinear dimensionality reduction. The effectiveness of the Supervised PCA methods in comparison with some state-of-the-art algorithms for dimensionality reduction is evaluated in relation to the statistical downscaling process of precipitation in a specific site using two soft computing nonlinear machine learning methods, Support Vector Regression and Relevance Vector Machine. The results demonstrate a significant improvement over Supervised PCA methods in terms of performance accuracy. © 2016, Springer-Verlag Berlin Heidelberg. 资助项目: NSERC, Natural Sciences and Engineering Research Council of Canada
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53350
Appears in Collections: 过去全球变化的重建
There are no files associated with this item.
作者单位: Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada; Department of Statistics and Actuarial Science, School of Computer Science, University of Waterloo, Waterloo, ON, Canada
Recommended Citation:
Sarhadi A.,Burn D.H.,Yang G.,et al. Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques[J]. Climate Dynamics,2017-01-01,48(2017-03-04)