Dan Liu, Ling Ke, Zhe Zeng, Shuo Zhang, Shanwei Liu
{"title":"基于机器学习的基于遥感和AIS数据的海雾对船舶近险碰撞的时空影响分析","authors":"Dan Liu, Ling Ke, Zhe Zeng, Shuo Zhang, Shanwei Liu","doi":"10.3389/fmars.2024.1536363","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"22 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based analysis of sea fog’s spatial and temporal impact on near-miss ship collisions using remote sensing and AIS data\",\"authors\":\"Dan Liu, Ling Ke, Zhe Zeng, Shuo Zhang, Shanwei Liu\",\"doi\":\"10.3389/fmars.2024.1536363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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. 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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 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.
期刊介绍:
Frontiers in Marine Science publishes rigorously peer-reviewed research that advances our understanding of all aspects of the environment, biology, ecosystem functioning and human interactions with the oceans. Field Chief Editor Carlos M. Duarte at King Abdullah University of Science and Technology Thuwal is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, policy makers and the public worldwide.
With the human population predicted to reach 9 billion people by 2050, it is clear that traditional land resources will not suffice to meet the demand for food or energy, required to support high-quality livelihoods. As a result, the oceans are emerging as a source of untapped assets, with new innovative industries, such as aquaculture, marine biotechnology, marine energy and deep-sea mining growing rapidly under a new era characterized by rapid growth of a blue, ocean-based economy. The sustainability of the blue economy is closely dependent on our knowledge about how to mitigate the impacts of the multiple pressures on the ocean ecosystem associated with the increased scale and diversification of industry operations in the ocean and global human pressures on the environment. Therefore, Frontiers in Marine Science particularly welcomes the communication of research outcomes addressing ocean-based solutions for the emerging challenges, including improved forecasting and observational capacities, understanding biodiversity and ecosystem problems, locally and globally, effective management strategies to maintain ocean health, and an improved capacity to sustainably derive resources from the oceans.