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...

Description complète

Détails bibliographiques

Auteurs principaux:	Shiu-Shin Lin, Kai-Yang Zhu, He-Yang Huang
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2025-01-01
Collection:	Atmosphere
Sujets:	climate change AR5 kernel principal component analysis machine learning deep learning
Accès en ligne:	https://www.mdpi.com/2073-4433/16/2/165
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!