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The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels

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  • Yue Wu
  • Ronald P. Pelot
  • Casey Hilliard

Abstract

There is a long history of studying the relationship between weather and maritime activities. This article analyzes the link between relative incident rate (RIR) and general weather factors within certain gridded areas and time periods. The study area, which encompasses a broad extent of Atlantic Canadian waters, includes fishing incidents recorded by the Canadian Coast Guard from 1997 to 1999. Methodologies used for traffic track generation in a geographical information system and aggregation of all relevant weather data needed for the statistical analyses are presented. Ultimately, a regression tree was built to illustrate the relationship between incident rate and the following six weather factors: wave height; sea surface temperature; air temperature; ice concentration; fog presence; and precipitation. Results from the regression tree reveal that the RIR defined as (incident number per area‐day)/(traffic amount per area‐day) across grid cells with incidents, increases as the weather conditions deteriorate in a general way, and the concentration of ice has the biggest influence on the magnitude of incident rates for a given level of traffic exposure. The results from this analysis may assist administrators of maritime traffic, especially those associated with fishing activities, through a better understanding of the influence on RIR of certain weather conditions within given areas in specific time periods.

Suggested Citation

  • Yue Wu & Ronald P. Pelot & Casey Hilliard, 2009. "The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels," Risk Analysis, John Wiley & Sons, vol. 29(7), pages 985-999, July.
    • Handle: RePEc:wly:riskan:v:29:y:2009:i:7:p:985-999
    DOI: 10.1111/j.1539-6924.2009.01217.x
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    References listed on IDEAS

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    1. Timothy G. Fowler & Eirik Sørgård, 2000. "Modeling Ship Transportation Risk," Risk Analysis, John Wiley & Sons, vol. 20(2), pages 225-244, April.
    2. Wayne Talley, 1999. "The safety of sea transport: determinants of crew injuries," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1365-1372.
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    1. Adland, Roar & Jia, Haiying & Lode, Tønnes & Skontorp, Jørgen, 2021. "The value of meteorological data in marine risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. Wang, Huanxin & Liu, Zhengjiang & Wang, Xinjian & Graham, Tony & Wang, Jin, 2021. "An analysis of factors affecting the severity of marine accidents," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. Jinfen Zhang & Ângelo P Teixeira & C. Guedes Soares & Xinping Yan & Kezhong Liu, 2016. "Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1171-1187, June.
    5. Christelle Viauroux & Ali Gungor, 2016. "An Empirical Analysis of Life Jacket Effectiveness in Recreational Boating," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 302-319, February.

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