Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data
Sea fog is a severe marine environmental disaster that significantly threatens the safety of maritime transportation. It is a major environmental factor contributing to ship collisions. The Himawari-8 satellite’s remote sensing capabilities effectively bridge the spatial and temporal gaps in data fr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1536363/full |
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| author | Dan Liu Ling Ke Zhe Zeng Zhe Zeng Shuo Zhang Shanwei Liu Shanwei Liu |
| author_facet | Dan Liu Ling Ke Zhe Zeng Zhe Zeng Shuo Zhang Shanwei Liu Shanwei Liu |
| author_sort | Dan Liu |
| collection | DOAJ |
| description | Sea fog is a severe marine environmental disaster that significantly threatens the safety of maritime transportation. It is a major environmental factor contributing to ship collisions. The Himawari-8 satellite’s remote sensing capabilities effectively bridge the spatial and temporal gaps in data from traditional meteorological stations for sea fog detection. Therefore, the study of the influence of sea fog on ship collisions becomes feasible and is highly significant. To investigate the spatial and temporal effects of sea fog on vessel near-miss collisions, this paper proposes a general-purpose framework for analyzing the spatial and temporal correlations between satellite-derived large-scale sea fog using a machine learning model and the near-miss collisions detected by the automatic identification system through the Vessel Conflict Ranking Operator. First, sea fog-sensitive bands from the Himawari-8 satellite, combined with the Normalized Difference Snow Index (NDSI), are chosen as features, and an SVM model is employed for sea fog detection. Second, the geographically weighted regression model investigates spatial variations in the correlation between sea fog and near-miss collisions. Third, we perform the analysis for monthly time series data to investigate the within-year seasonal dynamics and fluctuations. The proposed framework is implemented in a case study using the Bohai Sea as an example. It shows that in large harbor areas with high ship density (such as Tangshan Port and Tianjin Port), sea fog contributes significantly to near-miss collisions, with local regression coefficients greater than 0.4. While its impact is less severe in the central Bohai Sea due to the open waters. Temporally, the contribution of sea fog to near-miss collisions is more pronounced in fall and winter, while it is lowest in summer. This study sheds light on how the spatial and temporal patterns of sea fog, derived from satellite remote sensing data, contribute to the risk of near-miss collisions, which may help in navigational decisions to reduce the risk of ship collisions. |
| format | Article |
| id | doaj-art-2720ad46dac34f81a42eec6b7a489c14 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-2720ad46dac34f81a42eec6b7a489c142025-01-28T08:49:52ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011110.3389/fmars.2024.15363631536363Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS dataDan Liu0Ling Ke1Zhe Zeng2Zhe Zeng3Shuo Zhang4Shanwei Liu5Shanwei Liu6College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, ChinaNational Satellite Meteorological Center, China Meteorological Administration, Beijing, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, ChinaTechnology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation, Ministry of Natural Resources, Qingdao, Shandong, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, ChinaTechnology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation, Ministry of Natural Resources, Qingdao, Shandong, ChinaSea fog is a severe marine environmental disaster that significantly threatens the safety of maritime transportation. It is a major environmental factor contributing to ship collisions. The Himawari-8 satellite’s remote sensing capabilities effectively bridge the spatial and temporal gaps in data from traditional meteorological stations for sea fog detection. Therefore, the study of the influence of sea fog on ship collisions becomes feasible and is highly significant. To investigate the spatial and temporal effects of sea fog on vessel near-miss collisions, this paper proposes a general-purpose framework for analyzing the spatial and temporal correlations between satellite-derived large-scale sea fog using a machine learning model and the near-miss collisions detected by the automatic identification system through the Vessel Conflict Ranking Operator. First, sea fog-sensitive bands from the Himawari-8 satellite, combined with the Normalized Difference Snow Index (NDSI), are chosen as features, and an SVM model is employed for sea fog detection. Second, the geographically weighted regression model investigates spatial variations in the correlation between sea fog and near-miss collisions. Third, we perform the analysis for monthly time series data to investigate the within-year seasonal dynamics and fluctuations. The proposed framework is implemented in a case study using the Bohai Sea as an example. It shows that in large harbor areas with high ship density (such as Tangshan Port and Tianjin Port), sea fog contributes significantly to near-miss collisions, with local regression coefficients greater than 0.4. While its impact is less severe in the central Bohai Sea due to the open waters. Temporally, the contribution of sea fog to near-miss collisions is more pronounced in fall and winter, while it is lowest in summer. This study sheds light on how the spatial and temporal patterns of sea fog, derived from satellite remote sensing data, contribute to the risk of near-miss collisions, which may help in navigational decisions to reduce the risk of ship collisions.https://www.frontiersin.org/articles/10.3389/fmars.2024.1536363/fullHimawari-8 satellite datasea fognear missship collisionspatio-temporal pattern |
| spellingShingle | Dan Liu Ling Ke Zhe Zeng Zhe Zeng Shuo Zhang Shanwei Liu Shanwei Liu Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data Frontiers in Marine Science Himawari-8 satellite data sea fog near miss ship collision spatio-temporal pattern |
| title | Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data |
| title_full | Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data |
| title_fullStr | Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data |
| title_full_unstemmed | Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data |
| title_short | Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data |
| title_sort | machine learning based analysis of sea fog s spatial and temporal impact on near miss ship collisions using remote sensing and ais data |
| topic | Himawari-8 satellite data sea fog near miss ship collision spatio-temporal pattern |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1536363/full |
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