FogFusionNet: Coastal Sea Fog Prediction by Using a Multimodal Deep Learning Approach

In this study, we designed FogFusionNet, a multimodal sea fog prediction model, that used closed-circuit television (CCTV) images and multivariate time series observation (MTSO) data to predict three visibility classes-Normal visibility, Low visibility, and Sea fog-at 1-h intervals from the current...

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Bibliographic Details
Published in:IEEE access 2024, p.1-1
Main Authors: Son, Doo-Hwan, Chun, Minki, Kim, Young Taeg, Kang, Boonsoon, Kim, Kuk Jin, Han, Jin Hyun
Format: Article
Language:English
Subjects:
Atmospheric measurements
Closed-circuit television (CCTV) images
Coastal sea fog prediction
Coastlines
Data models
Feature extraction
Multimodal learning
Multivariate time series observation data (MTSO)
Numerical models
Ocean temperature
Predictive models
Sea measurements
Time series analysis
Visibility class
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Summary:In this study, we designed FogFusionNet, a multimodal sea fog prediction model, that used closed-circuit television (CCTV) images and multivariate time series observation (MTSO) data to predict three visibility classes-Normal visibility, Low visibility, and Sea fog-at 1-h intervals from the current time to 6-h in the future for a specific region. We applied weighted sampling and weighted loss to overcome the imbalance of each visibility class, and additionally evaluated the effect of replacing missing MTSO data. A total of 4 years of data regarding Incheon Port, which faces the Yellow Sea and is prone to sea fog, were collected for training and verifying FogFusionNet. Of these, 3 years of data were used for training FogFusionNet, and the remaining 1 year of data were used for verifying the performance of FogFusionNet. The prediction performance of FogFusionNet at 1-h intervals was 86.2% (0-h), 79.1% (1-h), 73.4% (2-h), 70.7% (3-h), 64.7% (4-h), 59.6% (5-h), and 49.3% (6-h), showing an average prediction performance of 69.0%. FogFusioneNet is expected to promote coastal safety and reduce economic losses due to coastal sea fog.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3401179