Spatial Modeling of Maritime Risk Using Machine Learning

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

Intelligent Geospatial Maritime Risk Analytics Using The Discrete Global Grid System

Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure

Virtual aids to navigation

There are many examples of master, bridge crew and pilot errors in navigation causing grounding under adverse circumstances that were known and published in official notices and records. Also dangerous are hazards to navigation resulting from dynamic changes within the marine environment, inadequate surveys and charts. This research attempts to reduce grounding and allision incidents and increase safety of navigation by expanding mariner situational awareness at and below the waterline using new technology and developing methods for the creation, implementation and display of Virtual Aids to Navigation (AtoN) and related navigation information. This approach has widespread significance beyond commonly encountered navigation situations. Increased vessel navigation activity in the Arctic and sub-Arctic regions engenders risk due, in part, to the inability to place navigational aids and buoys in constantly changing ice conditions. Similar conditions exist in tropical regions where sinker placement to moor buoys in sensitive environmental areas with coral reefs is problematic. Underdeveloped regions also lack assets and infrastructure needed to provide adequate navigation services, and infrastructure can also rapidly perish in developed regions during times of war and natural disaster. This research exploits rapidly developing advances in environmental sensing technology, evolving capabilities and improved methods for reporting real time environmental data that can substantially expand electronic navigation aid availability and improve knowledge of undersea terrain and imminent hazards to navigation that may adversely affect ship operations. This is most needed in areas where physical aids to navigation are scarce or non-existent as well as in areas where vessel traffic is congested. Research to expand related vessel capabilities is accomplished to overcome limitations in existing and planned electronic aids, expanding global capabilities and resources at relatively low-cost. New methods for sensor fusion are also explored to vi reduce overall complexity and improve integration with other navigation systems with the goal of simplifying navigation tasks. An additional goal is to supplement training program content by expanding technical resources and capabilities within the confines of existing International Convention on Standards for Training, Certification and Watchkeeping for Seafarers (STCW) requirements, while improving safety by providing new techniques to enhance situational awareness

Accident data-driven human fatigue analysis in maritime transport using machine learning

In maritime transport, fatigue conditions can impair seafarer performance, pose a high risk of maritime incidents, and affect safety at sea. However, investigating human fatigue and its impact on maritime safety is challenging due to limited objective measures and little interaction with other risk influential factors (RIFs). This study aims to develop a novel model enabling accident data-driven fatigue investigation and RIF analysis using machine learning. It makes new methodological contributions, such as 1) the development of a human fatigue investigation model to identify significant RIFs leading to human fatigue based on historical accident and incident data; 2) the combination of least absolute shrinkage and selection operator (LASSO) and bayesian network (BN) to formulate a new machine learning model to rationalise the investigation of human fatigue in maritime accidents and incidents; 3) provision of insightful implications to guide the survey of fatigue's contribution to maritime accidents and incidents without the support of psychological data. The results show the importance of RIFs and their interdependencies for human fatigue in maritime accidents. It takes advantage of available knowledge and machine learning to open a new direction for fatigue management, which will benefit the maritime fatigue investigation and provide insights into other high-risk sectors suffering from human fatigue (e.g. nuclear and offshore)

Methods for enhanced learning using wearable technologies. A study of the maritime sector

Maritime safety is a critical concern due to the potential for serious consequences or accidents for the crew, passengers, environment, and assets resulting from navigation errors or unsafe acts. Traditional training methods face challenges in the rapidly evolving maritime industry, and innovative training methods are being explored. This study explores the use of wearable sensors with biosignal data collection to improve training performance in the maritime sector. Three experiments were conducted progressively to investigate the relationship between navigators' experience levels and biosignal data results, the effects of different training methods on cognitive workload, trainees' stress levels, and their decision-making skills, and the classification of scenario complexity and the biosignal data obtained by the trainees. questionnaire data on stress levels, workload, and user satisfaction of auxiliary training equipment; performance evaluation data on navigational abilities, decision-making skills, and ship-handling abilities; and biosignal data, including electrodermal activity (EDA), body temperature, blood volume pulse (BVP), inter-beat interval (IBI), and heart rate (HR). Several statistical methods and machine-learning algorithms were used in the data analysis. The present dissertation contributes to the advancement of the field of maritime education and training by exploring methods for enhancing learning in complex situations. The use of biosignal data provides insights into the interplay between stress levels and training outcomes in the maritime industry. The proposed conceptual training model underscores the relationship between trainees' stress and safety factors and offers a framework for the development and evaluation of advanced biosignal data-based training systems

Automated Tugboat Assisted Docking of Large Vessels

The main research aspect behind this project was the problem in docking the ship to the port with limited space and resources available, causing number of onsite accidents and loss of resources. The main objective is to increase safety on ports, reduce pollution and increase fuel efficiency, also reduce the docking time for the vessels, which can be achieved by developing an autonomous docking system involving automated operations of tugboats, the port, and the vessel itself using control systems with limited involvement from human. This requires the development of a predictive control path for each component involved in the process. This is a long-term goal and requires a lot of research work and prototyping. In this thesis work, some studies will be carried out from past research work related to the problems focused on this project and the solutions provided to tackle these problems. Further data will be extracted along with all the essential equations and based on it; mathematical model will be developed as well as the development of path trajectory algorithm will be carried out. Also, its feasibility will be tested using simulation-based prototyping

Turbulence modelling and role of compressibility on oil spilling from a damaged double hull tank

The viscosity plays an important role, and a multiphase solver is necessary to numerically simulate the oil spilling from a damaged double hull tank (DHT). However, it is uncertain whether turbulence modelling is necessary, which turbulence model is suitable; and what the role of compressibility of the fluids is. This paper presents experimental and numerical investigations to address these issues for various cases representing different scenarios of the oil spilling, including grounding and collision. In the numerical investigations, various approaches to model the turbulence, including the large eddy simulation (LES), direct numerical simulation and the Reynolds average Navier-Stokes equation (RANS) with different turbulence models, are employed. Based on the investigations, it is suggested that the effective Reynolds numbers corresponding to both oil outflow and water inflow shall be considered when classifying the significance of the turbulence and selecting the appropriate turbulence models. This is confirmed by new lab tests considering the axial offset between the internal and the external holes on two hulls of the DHT. The investigations conclude for numerically simulating oil spilling from a damaged DHT that when the effective Re is smaller the RANS approaches should not be used and LES modelling should be employed; while when the effective Reynolds numbers is large, the RANS models may be used as they can give similar results to LES in terms of the height of the mixture in the ballast tank and discharge but costing much less CPU time. The investigation on the role of the compressibility of the fluid reveals that the compressibility of the fluid may be considerable in a small temporal-spatial scale but plays an insignificant role on macroscopic process of the oil spilling

Factors Affecting Collision & Grounding Losses in the UK Fishing Fleet

Merged with duplicate record 10026.1/660 on 13.20.2017 by CS (TIS)Examination of the literature reveals a paucity of dedicated research into collisions and groundings involving UK fishing vessels. The aim of this research was to provide answers to fundamental questions regarding the factors that contribute to fishing vessel traffic losses. Data for this study were gathered from a broad range of sources and an eclectic range of techniques employed in their analysis. The recent development of the UK fishing fleet and the pattern of losses from all causes is investigated for the period 1975 to 1994. Fishing vessel collision and grounding losses are then set in relative perspective by comparison with those arising from other causes. Aspects of the macro-environment in which the UK fishing fleet has operated since 1975 are examined and the results interpreted in the form of a comparative regional analysis. The micro-environment prevailing in the fishing fleet is exemplified through combining an array of observations made at sea on board working fishing vessels with questionnaire responses drawn from representative samples of British fishermen in 22 fishing ports around the country. A previously unattempted composite analysis of the circumstances of fishing vessel collision and grounding losses is presented and this allows for a number of conclusions to be drawn. A causal analysis technique is applied to fishing vessel casualties for the first time and leads to the identification of human factors as a more significant contributor to traffic losses than either technical or environmental factors. A novel programme of cross-validated observations of fishing vessel watch keepers in their working environment was pursued, providing data on how attention is allocated, workload levels at different stages in the fishing cycle and also on the watchkeeper's cognitive state while on duty. The thesis concludes with a wide ranging discussion and recommendations based on the research that could contribute to reducing loss of life and vessels in traffic events, made with due consideration for the physical and fiscal constraints that impinge upon the UK fishing fleet