Abstract:This study proposes a deep neural network (DNN) as a downscaling framework to compare original variables and nonlinear data features extracted by kernel principal component analysis (KPCA). It uses them as learning data for DNN downscaling models to assess future regional rainfall trends and uncertainties in islands with complex terrain. This study takes Taichung and Hualien in Taiwan as examples. It collects data in different emission scenarios (RCP 4.5, RCP 8.5) simulated by two Global Climate Models: ACCESS… Show more
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