Spatial Modeling of Maritime Risk Using Machine Learning

Abstract

Managing navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes, and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalized across diverse environments, or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterize the risk. Within this article a novel method for undertaking spatial modeling of maritime risk is proposed through machine learning. This enables navigational safety to be characterized while leveraging the significant volumes of relevant data available. The method comprises two key components: aggregation of historical accident data, vessel traffic, and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterize the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterizing maritime risk, with accuracies and area under curve scores in excess of 90% in most implementations. Furthermore, the ensemble tree‐based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decisionmakers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement.

Dynamic Multi-Period Maritime Accident Susceptibility Assessment Based on AIS Data and Random Forest Model

“…In addition, the Vessel Traffic Service (VTS) coverage in the study area has basically reached 100%; thus, the maritime supervision capacity is assumed to be the same. Finally, we summarized the relevant factors and found that ship and environmental data, such as the ship density, ship type, temperature, waves, etc., are the most widely used maritime influencing factors in relevant studies worldwide [2,3,8,18,19], as shown in Table 1.…”

“…The ship density (ShipDensity) [3,18] refers to the number of ships per unit area, somewhat reflecting the busyness in a water area. This study uses the number of vessels per unit to present the ship density.…”

“…The distance from the grid to the shore (DisShore) [18] was considered a risk for grounding-related hazards. We extracted a high-resolution world landmass shapefile from gadm.org (https://gadm.org/download_world.html (accessed on 24 March 2023)) and calculated the distance using the Geographical Information System (GIS) tool.…”

“…Recently, people have begun to use machine learning algorithms to solve related problems. Topics include "whether a region is an accident-prone area" [3,18,19], "whether a region is an accident-prone area for different severity" [3], and "dynamic monitoring ship safety in extreme weather events" [2]. These studies provide methods and atlases for maritime risk prediction and monitoring.…”

Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model

“…Machine learning (ML) techniques have been used in recent years to incorporate Artificial Intelligence (AI) into these models in order to mitigate natural disasters in general, and flood hazards in particular. These techniques include Logistic Regression (LR) [15,16], Support Vector Machines (SVMs) [17,18], Random Forest (RF) [19], and Artificial Neural Networks (ANNs) [20]. The loss of coastal wetlands owing to more frequent flooding demonstrates how susceptible they are to SLR [21].…”