Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed, China

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• 作者：Y.R. Fan^a ; G.H. Huang^a ; ^b ; ^{huangg@uregina.ca} ; Y.P. Li^b ; X.Q. Wang^a ; Z. Li^c ; L. Jin^d
• 关键词：Monthly streamflow prediction ; Principal component analysis ; Nonlinearity ; Maximal information coefficient ; Stepwise cluster analysis ; Probabilistic prediction
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：51
• 期：Complete
• 页码：280-293
• 全文大小：3995 K
• 卷排序：51

文摘

PCA and quantile regression are integrated into SCA to improve its efficiency. MIC is used to reveal nonlinearity between explanatory and response variables. sensitivity analysis is performed to identify the impacts of control parameters.