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"MARINE ACCIDENTS"
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Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data
According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.
Journal Article
The 12 worst shipwrecks of all time
Disasters are fascinating, awe-inspiring, and scary, all at the same time. Lean the facts about many of the worst disasters in human history. Then get some tips on how to prepare for disasters and stay safe.
Book
Human Error Analysis and Fatality Prediction in Maritime Accidents
The main objective of this paper is to underscore the significance of human error as a dominant cause of maritime accidents. The research is based on a comprehensive analysis of 247 maritime accidents, with the aim being to identify human failures occurring during onboard and port activities, as well as during the supervision process. The first step of the analysis was facilitating the Human Factor Analysis and Classification System (HFACS) as an advanced analytical tool for the identification and categorisation of human factors. Based on coding process, the most critical areas of human error are identified, based on the process of risk evaluation and assessment. Furthermore, a prediction model was developed for predicting the probability of fatality in a maritime accident. This model was constructed using logistic regression, considering the predominant causal factors and their interplay. Lastly, a set of preventive measures aimed at enhancing the efficiency and safety of maritime transport is provided.
Journal Article
Cove
\"Out at sea, in a sudden storm, a man is struck by lightning. When he wakes, injured and adrift on a kayak, his memory of who he is and how he came to be here is all but shattered. He will need to rely on his instincts, resilience, and imagination to get safely back to the woman he dimly senses is waiting for his return. This is an extraordinary, visceral portrait of a man locked in a struggle with the forces of nature\"--Page 4 of cover.
Book
The Role of the Human Factor in Marine Accidents
A common interest of all shipping industry stakeholders is safe and accident-free shipping. To reach that goal, one of the most important actions that can be done is to analyze previous marine accidents. It means finding causes of accidents and, based on the analysis results, implementing effective corrective measures that can help reduce such undesired events in the future and improve safety efforts in shipping. Since it is widely accepted that human error accounts for 80–85% of all marine accidents, the research was focused on the human factor analysis in marine accidents. In this paper, 135 marine accident reports recorded in the UK Marine Accident Investigation Branch (MAIB) database from 2010 to 2019 were analyzed. The analysis aimed to categorize causal factors and discover the ones that are the most common. The Human Factor Analysis and Classification System for Maritime Accidents (HFACS-MA) method was used to be able to do so. Furthermore, multiple linear regression was used to determine the relationship between the number of accidents and the most common HFACS-MA causal factors. The research revealed that the causes of marine accidents are primarily dependent on two human factor categories and confirmed that by influencing those human factors categories, the number of marine accidents could be reduced and shipping safety improved in general.
Journal Article
Framework for Process Analysis of Maritime Accidents Caused by the Unsafe Acts of Seafarers: A Case Study of Ship Collision
Accurately describing and evaluating the effects of unsafe acts on maritime accidents is critical to establishing practical accident prevention and control options. This paper proposes a framework for the probabilistic analysis of maritime accidents caused by seafarers’ unsafe acts by incorporating a navigation simulation and dynamic Bayesian network (DBN) modeling. First, the unsafe acts of seafarers are identified according to an in-depth analysis of global maritime investigation reports. Then, a navigation simulation experiment is designed to collect the ship-handling data of seafarers during hazardous accident scenarios. Consequently, a dynamic probabilistic model is proposed using a DBN to describe the phases of maritime accidents based on the navigation simulation experiment data. Furthermore, an evolution analysis of maritime accidents is conducted to explore the causal chain of such accidents through sensitivity analysis. The typical navigational accident-collision is chosen as the case to interpret the proposed framework, considering the formation process of ship collision risks, from the occurrence of ship collision risk (phase 1) to the close-quarters situation (phase 2) and to immediate danger (phase 3). This framework is applied to explore the causal chain of collision accidents caused by the unsafe acts of seafarers.
Journal Article
Sea of greed : a novel from the Numa files
\"After an explosion in the Gulf of Mexico destroys three oil rigs trying to revive a dying field, Kurt Austin and the NUMA Special Projects Team are tapped by the President of the United States to find out what's gone wrong. The trail leads them to a brilliant billionaire in the alternative energy field. Her goal is the end of the oil age; her company has spent billions developing the worlds' most advanced fuel-cell systems. But is she an environmental hero...or a rogue genetic engineer?\"-- Provided by publisher.
Book
Maritime Risk Assessment: A Cutting-Edge Hybrid Model Integrating Automated Machine Learning and Deep Learning with Hydrodynamic and Monte Carlo Simulations
In this study, a Hybrid Maritime Risk Assessment Model (HMRA) integrating automated machine learning (AML) and deep learning (DL) with hydrodynamic and Monte Carlo simulations (MCS) was developed to assess maritime accident probabilities and risks. The machine learning models of Light Gradient Boosting (LightGBM), XGBoost, Random Forest, and Multilayer Perceptron (MLP) were employed. Cross-validation of model architectures, calibrated baseline configurations, and hyperparameter optimization enabled predictive precision, producing generalizability. This hybrid model establishes a robust maritime accident probability prediction framework through a multi-stage methodology that ensembles learning architecture. The model was applied to İzmit Bay (in Türkiye), a highly jammed maritime area with dense traffic patterns, providing a complete methodology to evaluate and rank risk factors. This research improves maritime safety studies by developing an integrated, simulation-based decision-making model that supports risk assessment actions for policymakers and stakeholders in marine spatial planning (MSP). The potential spill of 20 barrels (bbl) from an accident between two tankers was simulated using the developed model, which interconnects HYDROTAM-3D and the MCS. The average accident probability in İzmit Bay was estimated to be 5.5 × 10−4 in the AML based MCS, with a probability range between 2.15 × 10−4 and 7.93 × 10−4. The order of the predictions’ magnitude was consistent with the Undersecretariat of the Maritime Affairs Search and Rescue Department accident data for İzmit Bay. The spill reaches the narrow strait of the inner basin in the first six hours. This study determines areas within the bay at high risk of accidents and advocates for establishing emergency response centers in these critical areas.
Journal Article