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DOI | 10.1007/s00382-016-3145-0 |
Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques | |
Sarhadi, Ali1; Burn, Donald H.1; Yang, Ge2; Ghodsi, Ali2 | |
2017-02-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2017 |
卷号 | 48 |
文章类型 | Article |
语种 | 英语 |
国家 | Canada |
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. |
英文关键词 | Dimensionality reduction Statistical downscaling Supervised Principal Component Analysis (S-PCA) Climate change Supervised learning |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000394150500035 |
WOS关键词 | RELEVANCE VECTOR MACHINE ; DAILY PRECIPITATION ; MODEL ; REGRESSION ; PREDICTION ; SELECTION ; DESIGN ; HYDROLOGY ; DISCHARGE ; ENSO |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/35911 |
专题 | 气候变化 |
作者单位 | 1.Univ Waterloo, Dept Civil & Environm Engn, Waterloo, ON N2L 3G1, Canada; 2.Univ Waterloo, Sch Comp Sci, Dept Stat & Actuarial Sci, Waterloo, ON N2L 3G1, Canada |
推荐引用方式 GB/T 7714 | Sarhadi, Ali,Burn, Donald H.,Yang, Ge,et al. Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques[J]. CLIMATE DYNAMICS,2017,48. |
APA | Sarhadi, Ali,Burn, Donald H.,Yang, Ge,&Ghodsi, Ali.(2017).Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques.CLIMATE DYNAMICS,48. |
MLA | Sarhadi, Ali,et al."Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques".CLIMATE DYNAMICS 48(2017). |
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