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Posted: October 30th, 2023
Prevention of Oil Spill from Shipping by Modelling of Dynamic Risk
Oil spill is one of the most serious environmental hazards caused by shipping activities. It can have devastating effects on marine ecosystems, human health, and economic activities. According to the International Tanker Owners Pollution Federation (ITOPF), there were 642 large oil spills (>7 tonnes) from tankers between 1970 and 2019, resulting in the release of about 5.8 million tonnes of oil into the sea . Moreover, there were thousands of smaller oil spills from various types of vessels, such as cargo ships, fishing boats, and cruise ships, which are often underreported or unnoticed .
To prevent oil spill from shipping, it is essential to understand the factors that influence the risk of oil spill and to develop effective measures to reduce the risk. However, the risk of oil spill is not static; it changes dynamically depending on various conditions, such as weather, traffic, vessel characteristics, human factors, and regulations . Therefore, a static risk assessment approach, which assumes that the risk is constant over time and space, may not capture the complexity and uncertainty of the real-world situation .
A dynamic risk modelling approach, on the other hand, can account for the temporal and spatial variations of the risk factors and provide more realistic and accurate estimates of the risk of oil spill. A dynamic risk model can also enable proactive and adaptive risk management strategies, such as real-time monitoring, early warning, and dynamic routing . For example, a dynamic risk model can help identify the areas and times with high risk of oil spill and suggest alternative routes or speed adjustments for vessels to avoid or mitigate the risk.
There are different methods and techniques for developing a dynamic risk model for oil spill prevention. Some examples are:
– Bayesian networks: A probabilistic graphical model that represents the causal relationships among the risk factors and the likelihood of oil spill. It can incorporate both quantitative and qualitative data and update the risk estimates based on new evidence or information .
– Agent-based modelling: A computational model that simulates the interactions among multiple agents (such as vessels, authorities, and environment) and their behaviours under different scenarios. It can capture the emergent phenomena and complex dynamics of the system .
– Fuzzy logic: A mathematical logic that deals with uncertainty and vagueness of the risk factors. It can handle imprecise or incomplete data and express the risk in linguistic terms (such as low, medium, or high) .
These methods and techniques can be combined or integrated to create a comprehensive and robust dynamic risk model for oil spill prevention. Such a model can provide valuable insights and guidance for decision-makers, stakeholders, and researchers in the field of maritime safety and environmental protection.
References:
: ITOPF (2020) Oil Tanker Spill Statistics 2019. Available at: https://www.itopf.org/fileadmin/data/Documents/Company_Lit/Oil_Spill_Stats_2020.pdf (Accessed: 30 October 2023).
: EMSA (2019) Annual Overview of Marine Casualties and Incidents 2019. Available at: http://emsa.europa.eu/implementation-tasks/accident-investigation/item/3836-annual-overview-of-marine-casualties-and-incidents-2019.html (Accessed: 30 October 2023).
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