Data assimilation with missing data in nonstationary environments for probabilistic machine learning models

Data assimilation with missing data in nonstationary environments for probabilistic machine learning models

In this study, we further develop the data assimilation framework proposed for probabilistic Machine Learning (ML) models, named Probabilistic Optimal Interpolation (POI), in nonstationary environments with missing data which are common in real-world situations. The dataset is based on a multi-scale...

Full description

Bibliographic Details
Main Authors: Wei, Yuying, Law, Adrian Wing-Keung, Yang, Chun
Other Authors: School of Civil and Environmental Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering::Civil engineering
Data Assimilation
Missing Data
Online Access:https://hdl.handle.net/10356/173067

Internet

https://hdl.handle.net/10356/173067

Similar Items