Spatio-Temporal Network for Sea Fog Forecasting

Jinhyeok Park, Young Jae Lee, Yongwon Jo, Jaehoon Kim, Jin Hyun Han, Kuk Jin Kim, Young Taeg Kim, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Sea fog can seriously affect schedules and safety by reducing visibility during marine transportation. Therefore, the forecasting of sea fog is an important issue in preventing accidents. Recently, in order to forecast sea fog, several deep learning methods have been applied to time series data consisting of meteorological and oceanographic observations or image data to predict fog. However, these methods only use a single image without considering meteorological and temporal characteristics. In this study, we propose a multi-modal learning method to improve the forecasting accuracy of sea fog using convolutional neural network (CNN) and gated recurrent unit (GRU) models. CNN and GRU extract useful features from closed-circuit television (CCTV) images and multivariate time series data, respectively. CCTV images and time series data collected at Daesan Port in South Korea from 1 March 2018 to 14 February 2021 by Korea Hydrographic and Oceanographic Agency (KHOA) were used to evaluate the proposed method. We compare the proposed method with deep learning methods that only consider temporal information or spatial information. The results indicate that the proposed method using both temporal and spatial information at the same time shows superior accuracy.

Original languageEnglish
Article number16163
JournalSustainability (Switzerland)
Issue number23
Publication statusPublished - 2022 Dec

Bibliographical note

Funding Information:
This research was supported by Korea Hydrographic and Oceanographic Agency (Tender notice of Busan Regional Public Procurement Service: 2021031390600), Brain Korea 21 FOUR, the Ministry of Science and ICT (MSIT) in Korea under the ITRC support program supervised by the IITP (IITP-2020-0-01749), and the National Research Foundation of Korea grant funded by the Korea government (RS-2022-00144190).

Publisher Copyright:
© 2022 by the authors.


  • deep learning
  • encoder-decoder structure
  • forecasting
  • multi-modal learning
  • sea fog

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Environmental Science (miscellaneous)
  • Geography, Planning and Development
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Management, Monitoring, Policy and Law
  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment


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