Fusing Climate Data Products using a Spatially Varying Autoencoder

arXiv:2403.07822v1 Announce Type: cross
Abstract: Autoencoders are powerful machine learning models used to compress information from multiple data sources. However, autoencoders, like all artificial neural networks, are often unidentifiable and uninterpretable. This research focuses on creating an identifiable and interpretable autoencoder that can be used to meld and combine climate data products. The proposed autoencoder utilizes a Bayesian statistical framework, allowing for probabilistic interpretations while also varying spatially to capture useful spatial patterns across the various data products. Constraints are …

abstract artificial artificial neural networks arxiv autoencoder autoencoders climate cs.lg data data products data sources however information machine machine learning machine learning models multiple networks neural networks products research stat.ap type