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 and Seoung Bum Kim ()
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Jinhyeok Park: School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea
Young Jae Lee: School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea
Yongwon Jo: School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea
Jaehoon Kim: School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea
Jin Hyun Han: Underwater Survey Technology (UST) 21, Incheon 21999, Republic of Korea
Kuk Jin Kim: Underwater Survey Technology (UST) 21, Incheon 21999, Republic of Korea
Young Taeg Kim: Korea Hydrographic and Oceanographic Agency, Busan 49111, Republic of Korea
Seoung Bum Kim: School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea

Sustainability, 2022, vol. 14, issue 23, 1-10

Abstract: 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.

Keywords: deep learning; encoder-decoder structure; forecasting; multi-modal learning; sea fog (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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