Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation

Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation

This study proposes a deep neural network (DNN) as a downscaling framework with nonlinear features extracted by kernel principal component analysis (KPCA). KPCA utilizes kernel functions to extract nonlinear features from the source climatic data, reducing dimensionality and denoising. DNN is used t...

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Bibliographic Details
Main Authors: Shiu-Shin Lin, Kai-Yang Zhu, He-Yang Huang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Atmosphere
Subjects:
climate change
AR5
kernel principal component analysis
machine learning
deep learning
Online Access:https://www.mdpi.com/2073-4433/16/2/165
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