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Marine transport is still famous and claimed to be part of human civilization, but in practice, marine vessels still experience accidents quite frequently, which can result in large losses. Therefore, this research aims to integrate multiple data sources on marine accidents, classify them to identify patterns, and create a model to forecast and prevent future accidents. The first step in the methodology is to connect several variables from multiple data sources and generate target variables. We then feed this ready data set into 10 machine learning algorithms to determine which one best suit the data type and quality. The training results provided four algorithms with the best performance, namely label spreading, label propagation, random forest, and XGB classifier algorithms. After comparing the training and testing results, we found that XGB performed slightly better than the other three models, where the developed model and dataset only had a performance of 70%-74% in predicting marine accidents in the corresponding class. [ABSTRACT FROM AUTHOR]

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members' performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea. [ABSTRACT FROM AUTHOR]

In view of the frequent occurrence of marine accidents and the complex interaction of various risk-influencing factors (RIFs), a data-driven method to risk analysis that combines association rule mining (ARM) and complex network (CN) analysis is proposed in this study. The efficient FP-Growth algorithm is applied to facilitate ARM to examine risk patterns that frequently occur in marine accidents. Subsequently, CN theory is employed to scrutinise the multifaceted role of various RIFs and their interactions in the complex marine accident system, which involves the basic characteristics of the network, the identification of key RIFs through the application of the weighted LeaderRank (WLR) algorithm, and a robustness analysis. The results of the study indicate that compared with random networks, marine accident networks exhibit a higher level of complexity, which brings challenges to safety prevention and control. Inadequate regulation, violations, and deficiencies in safety management systems are identified as key RIFs, stressing the urgency of improving supervision, strengthening law enforcement and strengthening the safety management system. This study may facilitate maritime safety management of maritime traffic and the development of risk analysis methods. [ABSTRACT FROM AUTHOR]

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members’ performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea.

Although the Brazilian Amazon is still one of the most well-conserved environments in the world, it contains an extensive network of waterways that are used by a wide range of vessels that transport both people and cargo, as well as undertaking other activities. To plan environmental risk disaster strategies here, the potential effects of technological disasters, including marine accidents, must be taken into consideration. This paper seeks to redress this by providing a review of the possible risks to the Amazon aquatic environment that could result from various types of marine accidents. While the Amazon region has been researched worldwide, both extensively and intensively, regarding land use, climate, and environmental characterization, work in this field is scarce. A review of the main environmental characteristics of the Amazon is provided, including a description of common marine accidents that have occurred in this region. The environmental impacts experienced by the aquatic environment due to marine accidents worldwide were reviewed from the scientific literature to examine potential risks to the Amazon waterways. It was found that marine accidents, such as fire/explosions and the sinking of ships with different types of cargo, can cause the spillage of oil and other hazardous materials, causing catastrophic damage to the aquatic environment. Changes in the hydrological characteristics of rivers, including unexpected changes in the weather, such as heavy rain and winds, may also influence pollutant dispersion. Initiatives to deal with these technological hazards in the Amazon basin should also consider the potential social and environmental impacts in the protected areas of this region. It is hoped that this paper can serve as a starting point toward the governance and planning of risk disaster management actions, which are aimed at reducing the negative environmental impacts caused by marine accidents in the region. [ABSTRACT FROM AUTHOR]

Jinhae Bay in South Korea is a common typhoon shelter, but there are no established criteria for the area or vessel capacity. The aim of this study was to determine the optimal capacity and arrangement of typhoon shelters for vessels in the sea area surrounding Jinhae Bay. The study identified several areas that could serve as typhoon shelters and conducted a survey with experienced VTS operators and ship operators to identify the best typhoon avoidance areas. The study found that the Japanese and Spanish design criteria for anchoring in strong winds were useful in computing the optimal capacity of typhoon shelters. A nesting algorithm based on the genetic algorithm and the No-Fit-Polygon theory was used to optimize the arrangement of shelters. The study found that the Jinhae Bay typhoon shelter can be effectively managed by arranging shelter-seeking vessels based on the nesting algorithm. The study contributes to supporting quantitative methodology-based decision-making and has practical significance for managing typhoon shelters in the Jinhae Bay area. Further research is needed to evaluate the proposed arrangement plan for typhoon shelters and confirm the validity of the results through simulation and practical implementation. Additionally, the time complexity for vessels to approach the anchorage should be considered in future studies. [ABSTRACT FROM AUTHOR]

Fishing boats are equipped to quickly rescue ships and save lives in the event of an incident at sea; therefore, determination of the incident location is imperative for a swift response. According to the 2021 marine accidents by ship use statistics, fishing and non-fishing boats accounted for 64.6 and 35.4%, respectively, of the total 3053 ships involved in an accident. In addition to V-pass and the Automatic Identification System (AIS), several other types of terminals exist; however, approximately 91% of all registered fishing boats use V-pass terminals. Therefore, it is essential to know the exact location of fishing boats. However, little research has been conducted on V-pass equipment. Therefore, in this study, marine experiments were conducted using V-pass and AIS terminals that are mainly used in fishing boats. To determine the exact location of an incident and rapidly respond and rescue, this study compared the data saved in the terminals with the data received by the Vessel Traffic Service center. In the event of a maritime incident, the radio shadow areas and causes of the error in the location transmitter must be investigated to quickly rescue the fishing boat and to determine the root cause of the incident, respectively. [ABSTRACT FROM AUTHOR]

This research aims to assess the main contributing factors that lead to a ship manoeuvring off track. For this reason, this paper reviews marine accident reports involving the failure to keep the vessel on the planned route. The study adopts the scoping review technique methodology. It started with the identification and screening of the accident reports as well as the selection of the reports based on the eligibility criteria. This was followed by the mapping and summarising of the reports based on the scope, before finally categorising the underlying factors through thematic analysis. While reviewing the accident records, it was noted that most accidents resulting from off-track manoeuvring were grounded, and in a few cases, it caused collisions with bridges, jetties, and buoys. The contributing factors to off-track manoeuvring were human factors, ship factors, and environmental factors. This study can enhance the navigation officers' awareness of the importance of staying on the course line and identify the probable cause of such occurrences. [ABSTRACT FROM AUTHOR]

Despite the development of advanced technology, marine accidents have not decreased. To prevent marine accidents, it is necessary to predict accidents in advance. With the recent development of artificial intelligence (AI), AI technologies such as deep learning have been applied to create and analyze predictive models in various fields. The purpose of this study is to develop a model for predicting the frequency of marine accidents using a long-short term memory (LSTM) network. In this study, a prediction model was developed using marine accidents from 1981 to 2019, and the proposed model was evaluated by predicting the accidents in 2020. As a result, we found that marine accidents mainly occurred during the third officer's duty time, representing that the accidents are highly related to the navigator's experience. In addition, the proposed LSTM model performed reliably to predict the frequency of marine accidents with a small mean absolute percentage error (best MAPE: 0.059) that outperformed a traditional statistical method (i.e, ARIMA). This study could help us build LSTM structures for marine accident prediction and could be used as primary data to prevent the accidents by predicting the number of marine accidents by the navigator's watch duty time. [ABSTRACT FROM AUTHOR]

Jinhae Bay in South Korea is a common typhoon shelter, but there are no established criteria for the area or vessel capacity. The aim of this study was to determine the optimal capacity and arrangement of typhoon shelters for vessels in the sea area surrounding Jinhae Bay. The study identified several areas that could serve as typhoon shelters and conducted a survey with experienced VTS operators and ship operators to identify the best typhoon avoidance areas. The study found that the Japanese and Spanish design criteria for anchoring in strong winds were useful in computing the optimal capacity of typhoon shelters. A nesting algorithm based on the genetic algorithm and the No-Fit-Polygon theory was used to optimize the arrangement of shelters. The study found that the Jinhae Bay typhoon shelter can be effectively managed by arranging shelter-seeking vessels based on the nesting algorithm. The study contributes to supporting quantitative methodology-based decision-making and has practical significance for managing typhoon shelters in the Jinhae Bay area. Further research is needed to evaluate the proposed arrangement plan for typhoon shelters and confirm the validity of the results through simulation and practical implementation. Additionally, the time complexity for vessels to approach the anchorage should be considered in future studies.

Fishing boats are equipped to quickly rescue ships and save lives in the event of an incident at sea; therefore, determination of the incident location is imperative for a swift response. According to the 2021 marine accidents by ship use statistics, fishing and non-fishing boats accounted for 64.6 and 35.4%, respectively, of the total 3053 ships involved in an accident. In addition to V-pass and the Automatic Identification System (AIS), several other types of terminals exist; however, approximately 91% of all registered fishing boats use V-pass terminals. Therefore, it is essential to know the exact location of fishing boats. However, little research has been conducted on V-pass equipment. Therefore, in this study, marine experiments were conducted using V-pass and AIS terminals that are mainly used in fishing boats. To determine the exact location of an incident and rapidly respond and rescue, this study compared the data saved in the terminals with the data received by the Vessel Traffic Service center. In the event of a maritime incident, the radio shadow areas and causes of the error in the location transmitter must be investigated to quickly rescue the fishing boat and to determine the root cause of the incident, respectively.

To manage and control ship collision risk, this paper aims to develop a quantitative risk analysis approach based on complex networks and Decision-making Trial and Evaluation Laboratory (DEMATEL), to study the evolution of ship collision accidents. Firstly, through the search, selection and analysis of accident investigation reports of ship collisions in the world, 46 Risk Influential Factors (RIFs) were identified, and a network model for the evolution of ship collision was established. Secondly, a risk analysis of the evolution model for ship collision accidents was conducted by utilizing the topology characteristics of the complex network theory. Thirdly, a robustness analysis was used to identify 10 key RIFs involved in ship collisions from a global network perspective, and the grey relation analysis technique was employed to verify the key RIFs. Finally, the DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality, and corresponding risk control measures were proposed to mitigate those key RIFs. The research results provide theoretical support for identifying the key RIFs and controlling the risk transmission of ship collision, and have practical significance for preventing the occurrence of ship collision accidents and ensuring the safe navigation of ships. • A complex network model for the evolution of ship collision was established. • A risk analysis of the evolution model for ship collision accidents was conducted. • A robustness analysis was used to identify 10 key RIFs from a global network perspective. • DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality. • The corresponding risk control measures were proposed to mitigate those key RIFs. [ABSTRACT FROM AUTHOR]

• A systematic study on the prediction of the severity of marine accidents was conducted. • A research framework for predicting marine accident severity was proposed. • A two-stage feature selection method was proposed and evaluated with strong interpretability. • Six well-established machine learning models were compared to generate a benchmark. • Risk control measures were quantitatively assessed based on counterfactual analysis. Although many studies have focused on the occurrence likelihood of marine accidents, few have focused on the analysis of the severity of the consequences, and even fewer on the prediction of the severity. To this end, a new research framework is proposed in this study to accurately predict the severity of marine accidents. First, a novel two-stage feature selection (FS) method was developed to select and rank Risk Influential Factors (RIFs) to improve the accuracy of the Machine Learning (ML) model and interpretability of the FS. Second, a comprehensive evaluation method is proposed to measure the performance of the FS methods based on stability, predictive performance improvement, and statistical tests. Third, six well-established ML models were used and compared to measure the performance of different predictors. The Light Gradient Boosting Machine (LightGBM) was found to have the best predictive performance for the severity prediction of marine accidents and was treated as the benchmark model. Finally, LightGBM was used to predict accident severity based on the RIFs selected by the proposed FS method, and the effect of risk control measures was counterfactually analysed from a quantitative perspective. This innovative study on the use of improved ML approaches can effectively analyse and predict the severity of marine accidents, providing a novel methodology for and triggering a new direction for using Artificial Intelligence (AI) technologies in safety assessment and accident prevention studies. The source code is publicly available at : https://github.com/FengYinLeo/PGI-SDMI. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the possible causes of collision accidents by using Fault Tree Analysis (FTA). In this study, we were able to examine the potential cause of collision accidents, then develop a fault tree model of the root causes of the accidents using the FTA approach, and finally provide the probability of basic event combinations leading the occurrence of accidents. A total of 62 collision accident reports providing detailed information about the causes of accidents were obtained by Marine Accident Investigation Branch (MAIB) between 2005 and 2020. The study found that most of the factors (E1/Misuse of navigational tools, E3/COLREG Rule-5 (Look-out)) that had the greatest effect on the collision were mainly due to the inadequacy to keep a safe navigation watch. For that reason, the findings of the study are very important in terms of determining the strategies to eliminate the risks for future accident prevention. For future studies, it should collect more accidents data on varying types of ships to improve their prediction performance, incorporate expert opinions with fuzzy evidence into the model to minimize uncertainties, and enhance model expressiveness. In addition, alternative risk assessment methods should be applied considering other types of vessels for better comparisons. [ABSTRACT FROM AUTHOR]

In general, life jackets are worn by people in critical situations on the water to rise to the surface without falling into water, and life jackets simply serve to prevent sinking, but do not actively help rescue disaster areas in critical situations early. In order to solve this problem, a device that automatically generates a distress signal in an emergency situation was developed. When the survivor is in hand for a while, the distress signal generator is automatically separated from the life jacket, allowing information such as location values and other information to be transmitted wirelessly and a rescue signal using LED light. As a result of the experiment, when submerged in water, the life jacket and the distress signal generator were automatically separated, and the result of wireless transmission of the coordinate value of the location received by the GPS was confirmed. By using this, the location of the distress or missing person can be identified, which can be quickly replaced in case of an emergency [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article explores the use of ontology for semi-automatic marine vessel navigation and ship-to-ship communication to mitigate collision risk. Semi-automatic vessel communication is a step towards automatic communication for autonomous ships. Examples of how such communication can be used is discussed, based on a comprehensive analysis of selected marine collisions, with particular attention to the communication conducted on ships. The effectiveness of such communication was assessed and compared. The suggested solutions are based on the review of official reports from accident investigations. The novelties of this work include original ontologies and interfaces. Through this work, it could be possible to fully automate communication processes between ships. In future work, the research results in this work will be used to create a system of automatic communications for manned and autonomous vessels. [ABSTRACT FROM AUTHOR]

The International Maritime Dangerous Goods (IMDG) Code training is mandatory for all shoreside personnel involved in the handling and transport of dangerous cargoes by sea. This study aims to measure and evaluate the port workers’ dangerous cargo transport general awareness level. For this purpose, the “Dangerous Cargo Transport General Awareness Questionnaire” was developed and applied to trained 100 port workers face-to-face. The questionnaire was proven to be valid and reliable by content validity, construct validity, internal consistency reliability, and testretest reliability analyses. Four factors were determined as a result of Explanatory Factor Analysis. Discriminant analyses were performed using the Student’s t-test and One Way ANOVA test. Pearson correlation analysis was carried out to determine the relationship between factors. Simple linear regression analysis was used for modeling the relationship between factors. SPSS 24.0 was utilized to conduct the analyses. According to analysis results, port workers’ dangerous cargo transport general awareness does not differ depending on age, position, and education. There is a weak positive or a moderate positive correlation between factors. Port workers have an average and above-average level of general awareness. It is highlighted that increasing this level is possible by increasing the frequency of dangerous cargo transport training. Suggestions are offered for more effective training. [ABSTRACT FROM AUTHOR]

Despite the development of advanced technology, marine accidents have not decreased. To prevent marine accidents, it is necessary to predict accidents in advance. With the recent development of artificial intelligence (AI), AI technologies such as deep learning have been applied to create and analyze predictive models in various fields. The purpose of this study is to develop a model for predicting the frequency of marine accidents using a long-short term memory (LSTM) network. In this study, a prediction model was developed using marine accidents from 1981 to 2019, and the proposed model was evaluated by predicting the accidents in 2020. As a result, we found that marine accidents mainly occurred during the third officer’s duty time, representing that the accidents are highly related to the navigator’s experience. In addition, the proposed LSTM model performed reliably to predict the frequency of marine accidents with a small mean absolute percentage error (best MAPE: 0.059) that outperformed a traditional statistical method (i.e, ARIMA). This study could help us build LSTM structures for marine accident prediction and could be used as primary data to prevent the accidents by predicting the number of marine accidents by the navigator’s watch duty time.

Detecting the risk of collision is a very important step to prevent marine accidents. For detecting the risk of collision, radar plotting is often used. Based on therelative position and motion of two ships, the risk of collision between them can be evaluated. However, the present radar equipmentis not supported to detect the risk of collision between two target ships from the observation data measured by a third party. This causes difficulties for officers of shore stations, when evaluating the marine traffic situation to maintain the safety of navigation. To solve this problem, it is necessary to develop a method to evaluate the risk of collision between two target ships from the observation data measured by the shore station radar (the third party). In this article, the development of such method is introduced.. [ABSTRACT FROM AUTHOR]

This study discusses the marine accident involving the Cosco Busan, the container ship that hit the base of the Delta Tower of the San Francisco-Oakland Bay Bridge in November 2007. An analysis of the elements resulting in the accident and its consequences has been carried out, followed by an analysis of the navigational risk using mathematical and tabular values. Mathematical values refer to the navigational risk in a specific sailing area, while tabular values refer to the navigational risk using a risk assessment. The main goal of this research was to identify and propose new measures that are correlated with a risk assessment. These measures should be applied in areas where an accident takes place so that future marine accidents can be reduced. [ABSTRACT FROM AUTHOR]

An opposing sea was generated in an experimental wave basin. Here, an opposing sea is defined as a sea in which the wave spectrum has two peaks that are separated almost 180° in direction. Such an opposing sea can be observed, for example, when wind waves coexist in the opposite direction with swells. The opposing sea generation is based on an actual sea spectrum hindcast by a third-generation wave model. The experiment was carried out in an actual sea model basin (ASMB) at National Maritime Research Institute. The ASMB is fully surrounded by 382 flap-type wave makers with incident wave absorbing capacity. The estimation of the directional spectrum by the maximum likelihood method (MLM) revealed that the opposing sea was successfully generated in the basin, although an analysis of the corresponding numerical wave field indicated that the MLM inherently estimated the spectrum with wider directional spreading. The reproduction of the opposing sea in this study indicates that any deep-sea wave field with arbitrary directional spectrum can be reproduced in such a wave basin with absorbing wave makers on the periphery. [ABSTRACT FROM AUTHOR]

This paper analyses the marine accident reports published by the USA National Transportation Safety Board (NTSB) between June 1975 and September 2017, in order to ascertain the exact influence of the crew and/or other people involved (pilots, company, etc.) on the cause of the incident and to find out the types of errors made by them. Additionally, the mistakes and those involved are analysed together to verify the correct implementation of international regulations concerning seafarers. The results suggest that crew error is relatively uncommon in cargo and passenger fleets. This points to the effectiveness of the International Convention on Standards of Training, Certification and Watchkeeping (STCW convention). However, taking into consideration the high percentage of human error due to failure in communication and misjudgements during navigation in pilot waters, it may be deduced that the coordination among those on the bridge may be improved. On the other hand, crew error on board tugs exceeds 50%. This increase seems to be due to the difficulties of navigation in narrow waterways.

Modern ships are designed and built according to advanced safety rules and regulations derived from historic fatal marine accidents. Many large ships, especially naval vessels and cruise ships, are equipped with various kinds of computerized systems called damage control systems (DCSs) for facing emergency situations. These systems are designed to mitigate the consequence of injuries to crewmembers and the mission capability of a ship. However, crews still take charge of the main role in controlling emergency situations. All emergency response work has to follow related guidelines and should be done in the early phase of an emergency situation. For this, appropriate information for decision making and simplified communication methods are important. In this paper, a coded shortcut key basis onboard DCS operation is suggested to help crews who have to use a complex computerized DCS in urgent situations. The coded shortcut keys are considered for effective communication among the emergency response team members. [ABSTRACT FROM AUTHOR]

This study examines and analyzes marine accidents that have occurred over the past 20 years in the Black Sea. Geographic information system, human factor analysis and classification system (HFACS), and Bayesian network models are used to analyze the marine accidents. The most important feature distinguishing this study from other studies is that this is the first study to analyze accidents that have occurred across the whole Black Sea. Another important feature is the application of a new HFACS structure to reveal accident formation patterns. The results of this study indicate that accidents occurred in high concentrations in coastal regions of the Black Sea, especially in the Kerch Strait, Novorossiysk, Kilyos, Constanta, Riva, and Batumi regions. The formation of grounding and sinking accidents has been found to be similar in nature; the use of inland and old vessels has been highlighted as important factors in sinking and grounding incidents. However, the sequence of events leading to collision‐contact accidents differs from the sequence of events resulting in grounding and sinking accidents. This study aims to provide information to the maritime industry regarding the occurrence of maritime incidents in the Black Sea, in order to assist with reduction and prevention of the marine accidents. [ABSTRACT FROM AUTHOR]

Copyright of Inonu University Law Review / İnönü Üniversitesi Hukuk Fakültesi Dergisi is the property of Inonu University Law Review (IULR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow‐ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors. [ABSTRACT FROM AUTHOR]

This study used a spatial distribution analysis to identify the risky areas in the Çanakkale Strait - one of the narrowest waterways with high marine traffic - in terms of ship accidents. To accomplish this, a point density analysis, as part of the geographic information systems (GIS) methodology, was used to create accident density distributions and thematic maps. A total of 162 marine accidents in the Çanakkale Strait between 2007 and 2018 were taken into account. Detailed technical data of accidents were obtained from accident reports provided by the Turkish Maritime Search and Rescue Coordination Center (TMSRCC). The results showed that, among the 162 cases, bulk carriers have the highest accident rate, and most accidents at the Nara Turn were caused by engine failure. In addition, accidents due to navigational failure occurred in every region of the strait. Remarkably, factors directly and indirectly related to human error were still the determining factors in marine accidents. As a result, the risk of marine accidents was higher in the narrow portions of the waterway, and navigation was difficult, especially in areas such as the Nara Turn Point. In future research, it is recommended to consider a wider period of activities using different methods to provide more comprehensive results. [ABSTRACT FROM AUTHOR]

This study addresses human elements in marine accidents that cause many deaths, environmental pollution, and economic losses. The aim of this study is to identify various human elements that cause marine accidents and to visualize their interaction with each other on a two-dimensional plane. For this purpose, a two-stage study was conducted: the first was a pilot study in which the performance of different dimensionality reduction and clustering algorithms was tested, and the second was the phase in which 2050 textual expressions corresponding to human elements in 871 marine accidents were analyzed with the algorithms featured in the pilot study. The textual expressions were converted into vectors with the BERT model, dimensionality reduction was performed with UMAP, and clustering was performed with the k-Means algorithm. The results showed that the most common type of error and the one that interacts the most with others are navigational errors. It has also been observed that a cause-effect relationship can be established between the clusters such as " fatigue" -" cognitive dysfunction " and "f alse observation "-" violation of navigation rules ". This study contributes to taking more specific safety measures as it directly reveals human elements specific to marine accidents rather than using generic human error classifications. • As a result of dimensionality reduction and clustering analysis, 19 different human elements were detected. Navigation errors have been determined to be the most common type of error. • By using marine accident reports, marine-specific errors were easily detected. • The relationships between the elements are also shown with graphics. It has been determined that there is both a visual and semantic cause-effect relationship between the clusters " Incompetent Engine Room Staff " - " Poor Maintenance and Equipment Failure ", " Fatigue " - " Cognitive Dysfunction " and" Perceptual Error " - " Violation of Navigation Rules ". • The error graph created as a result of the analysis visually highlights the interaction between errors. [ABSTRACT FROM AUTHOR]

The purpose and scope of this paper is to describe anchoring procedures and typical human errors that are the cause of many marine accidents related to the anchoring of vessels and their manoeuvring in anchorage areas. In this paper the author focuses on typical marine accidents recorded for very large crude carriers (VLCC). As a result of the analyses, it can be seen that in the vast majority of cases these accidents are caused by human error and are related to the violation of accepted maritime anchor practices and a failure to observe the relevant safety and security procedures. The consequences of the accidents vary from the minor (e.g. slight structural damage to the anchor winches or other marine equipment) to the serious, which result in dry dock repairs due to hull damage, loss of stability and/or loss of navigability. The described cases refer to both favourable and extremely unfavourable hydro meteorological conditions, the latter including strong winds, currents and waves within the confined anchorage area.

The law of general average is one of the oldest institutions in maritime law, which is still applicable in contemporary shipping. Although the role and function of general average have naturally changed throughout centuries of its history (the main reasons therefor being the popularization of maritime insurance, which evolved much later than the general average) and general average has been and is subject to criticism pointing to its anachronistic nature and redundancy in today’s maritime economic realities. However practice shows that general average continues to resist these accusations and is commonly used in the practice of maritime commercial trade. This article deals with the issue of general average in light of Polish maritime law regulations, the essence and specificity of these legislative solutions concerning the safety of shipping in Poland, and also gives examples of events that occurred at sea that permit the announcement of an act of the general average. This article also presents considerations with regard to arguments in favour of maintaining the institution of general average as a legal instrument used in shipping, as well as critical voices emphasizing the need to eliminate general average as an institution of maritime law aimed at distributing losses, incurred to avoid a common peril, to the participants of a maritime adventure. [ABSTRACT FROM AUTHOR]

Abstract Concerns have been raised around pilotage in Korea due to a rise in marine accidents in the 2010s. Since the late 2000s, a debate has been sparked on the most suitable age of retirement for Korean pilots. The debate has focused on the extension of retirement age of pilots from 65 to 68 and whether this will affect the probability of marine accidents. Therefore, it is crucial to calculate the probability of marine accidents in relation to different age groups of pilots. After collecting the data of marine accidents caused by pilot's negligence, the study suggests two measurements of probability of marine accidents during pilotage: on the basis of the number of pilotage services and the hours of pilotage services. The analysis finds that age is not the exclusive cause of pilotage marine accidents by the age group over 65. [ABSTRACT FROM AUTHOR]

In the maritime transportation services industry, marine accidents may lead to fatalities, injuries, and property losses. Coastal ferry operators experience marine accidents and must pay attention to safety to guarantee the sustainability of their business. This study is aimed at analyzing the operational efficiency of coastal ferry operators in Korea from a safety perspective. We designed two slack-based measure of efficiency (SBM) models. One is a normal SBM, which includes only the total passenger volume as the desirable output. The other is a safety-constrained SBM, which includes marine accident records as an undesirable output with the desirable output of passenger transportation performance. We selected 44 coastal ferry operators in Korea that have been continuously operating for five years (2013–2017) as decision-making units (DMUs) and compared their operational efficiency scores. The results showed that the impact of marine accidents on business is greater in DMUs with lower transportation sales than in those with higher sales. This suggests that, while it is important for the government to strengthen safety regulations, a combination of policies that also help small ferry operators to stay in business in the long term is necessary to reduce marine accidents effectively while improving efficiency.

This paper investigates the process of marine accident analysis for the official interpretation to define the rate of defects of vessels in the intended case and proposes the pairwise comparison approach which is frequently used in AHP method. The assessment of marine accidents is a complicated and sophisticated problem which needs a professional and fair judgment based on the existing evidences and navigational specs. However, human judgment is limited and it is usually problematic to evaluate several aspects of a case. The pairwise comparison method is first suggested to simplify decision making process for ranking or selecting an item among the alternatives and it is an essential part of analytic hierarchy process. By utilizing the pairwise comparison, the defect rate of a snapshot of marine accident is calculated in a stepwise approach.

With the increase in the carrying capacity of passenger vessels parallel to technological developments over the last 25 years, accidents resulting in loss of lives have increased. Thus, accidents involving passenger vessels have become a major issue of concern in the maritime industry. In this study, 70 ship collision & contact accidents involving passenger vessels between 1991 and 2015 were examined. Unlike other studies in the literature, this investigation proposes a customized Human Factors Analysis and Classification System for Passenger Vessel Accidents (HFACS-PV) to facilitate analysing the human factor in passenger vessel accidents. In addition to the core HFACS structure, an additional operational condition level has been defined. The violations framework has been divided into the three sub-categories of rule violations, procedure violations, and abuse of authority, instead of the two broad categories of routine and exceptional violations. Abuse of authority is an intentional violation made knowingly and wilfully, therefore, abuse of authority has been considered separately. Furthermore, appropriate modifications have been made to the headings under the second level of HFACS-Preconditions for Unsafe Acts for compliance with the maritime industry. [ABSTRACT FROM AUTHOR]

• The study conducted a bibliometric and correspondence analysis to investigate scientific awareness. • With the help of correspondence analysis, it is aimed to examine accidents' consequences and severity based on countries. • Literature published in European countries related to marine accidents has been reviewed by using a bibliometric analysis. • The results show that scientific awareness of researchers in countries where serious and very serious marine accidents occur is high. • It is observed that there is gap between European countries in scientific manner and lack of harmonisation in scientific awareness. Marine accidents are a significant issue that can lead to loss of life, damage to ships and cargo, and harm to the marine environment. To gain better understanding of scientific awareness of marine accidents occurred in European countries, this study conducted a bibliometric and correspondence analysis of the scientific literature. Bibliometric analysis was employed to examine various publications, which were released during the period between 2012 and December 2022. Moreover, correspondence analysis was used to classify and analyze marine accidents based on the severity and the consequence of the accidents. The findings indicate that scientific awareness of researchers in countries where serious and very serious marine accidents occur is also high. However, Norway stands out as the country with the highest scientific awareness of researchers despite experiencing marine accidents with less serious. The most significant factor contributing to the prominence of researchers here is their collaborations with researchers from other countries. Overall, this study sheds light on the need for further research and action to improve marine accident prevention. Collaborative efforts involving researchers, maritime stakeholders, and policymakers are necessary to address the complex challenges of marine accidents and to ensure the maritime safety and protection of the marine environment. [ABSTRACT FROM AUTHOR]

In order to analyse the research evolution and knowledge frontier in the research of marine accidents, 491 literatures on marine accidents in the Web of Science database from 2000 to 2022 are taken as data sources. Integrated with literature analysis of traditional method, CiteSpace and VOSviewer are then jointly used for the development of the knowledge network map and cluster analysis, and the knowledge of network map, research hotpots, research evolution and knowledge frontiers is obtained. It is found that there is a close cooperative relationship among journals, researchers, research institutions and countries or regions. According to the subjects and methods, the study of marine accidents can be divided into two parts: the analysis of the influential factors and accident consequences, as well as the methodology development of traditional and emerging technology. In this context, the analysis of human factors in remote-controlled ships, the prevention of accidents in Arctic waters have become research hotspots, while emerging accident analysis methods such as machine learning and big data mining also have shown powerful insights in the analysis of marine accidents. In terms of innovation, the bibliometric approach enhances the ability to handle large literature databases and conduct network analysis. Moreover, this study visualises collaborative networks, analyses evolution trends, reveals the research hotpots, and conducts a comparison and discussion of mainstream approaches in marine accident research. As a result, this study provides a theoretical basis and implementation direction for the development of maritime safety. • A systematic review and outlook of marine accident research is conducted. • A more comprehensive literature database of marine accident research is established. • The research hotpots and knowledge frontiers of marine accident research are identified. • The contributions, innovations and the future directions of marine accident research are discussed. [ABSTRACT FROM AUTHOR]

Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

The 2010 Manila amendments to the Standards of Training, Certification and Watchkeeping Code (STCW) was adopted by the idea to render the profession more attractive to the seafarers, particularly to cadets. It is possible to ensure greater attraction only by providing suitable and safe working conditions on board to cadets. This study analyses occupational accidents and near misses encountered by ocean going deck cadets, who received A-II/I training, during their sea training. The aim of this study was to determine causal factors influencing work accidents and to propose several recommendations for the safety of deck cadets. A total of 857 officer candidates, who received maritime education at university level in Turkey, were interviewed. The Analytic Hierarchy Process (AHP) method was used in the study to analyse the occupational accidents. As a result of this study on deck cadets, the most risky areas for work accidents were found to be the deck (39.9%), cargo areas (35.7%), areas used for manoeuvring operations (including winch areas and areas in which berthing, unberthing, and anchoring operations take place) (16.1%), and accommodation areas (8.3%). The most important root causes for occupational accidents were identified as not using personal protective equipment (24.2%), haste (22.6%), and presence in inappropriate places (13.6%). This study offers some important insight into the prevention of occupational accidents, and includes suggestions and advisory opinions of sector representatives. As a result of this study, several recommendations for the prevention of accidents are proposed. [ABSTRACT FROM AUTHOR]

Hong, D.-B. and Yang, C.-S., 2017. Designing an algorithm to generate dynamic intentional grounding zones (DIGOs) for distressed passenger ships in coastal regions. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 134-138. Coconut Creek (Florida), ISSN 0749-0208. In an emergency case, a passenger ship can be grounded on coastal zone in order to prevent sinking of the vessel and to minimize the injuries and casualties. For the safe and efficient mustering and evacuation of cruise ships, the innovative and reliable systems are needed and discussed in the International Maritime Organization (IMO) for several decadal years. In this paper a novel method is suggested for designating a safe grounding zone, here Dynamic Intentional Grounding Zones (DIGOs), in real-time situation. Thus, DIGO will be a place around passenger ship for effective emergency evacuation in case of uncontrolled marine accident. The research area is the southwest coastal region of Korea including Jindo Island. In this region, there are many passenger ships regularly travelling between islands, large intertidal areas, and eddies caused by rapid changes in currents. DIGOs are automatically calculated in three-dimensional space by combining shipping information (position, draft, etc.) and marine environments (water depth, sea-bottom sediments, and tide level) in real-time. The results of this study can be used to support captains' decision-making during maritime accidents. [ABSTRACT FROM AUTHOR]

• A semi-automatic model is developed for hazard and accident cause identification. • We explore and analyze the semantic relationship between the hazards of marine accidents. • The importance of both hazards and accident causes is calculated and ranked. • We gain accident causes distribution in 40 different accident scenarios by using topic model. In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions. [ABSTRACT FROM AUTHOR]

Currently, globalization of the world economy has also resulted in a shipping volume increase. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features such as islands are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites, such as in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest pixel value between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance between rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.

Šibenik is a large and one of the oldest and best protected natural harbor on the Croatian Adriatic coast. The Old Port is located in the submerged estuary of the river Krka. Limited available room for maneuvring within the harbour, new passenger terminal and part of the port for handling different types of cargo reduce the space for safe navigation and mooring / unmooring of merchant and cruise ships. Approach to the port of Šibenik from the open sea, especially the navigation through St. Anthony channel and maneuvering inside the waters of the city port present a particular challenge to each seafarer. As far as safety is concerned, the mentioned circumstances significantly contribute to increasing the threat of possible maritime accidents. Therefore, there is a need to evaluate the potential threats of maritime accidents in the Šibenik St. Anthony channel. To determine the threat of maritime accidents a qualitative method was used: ranking of threats. Each maritime accident in the narrow area of the St. Anthony channel and the port of Šibenik could result in severe consequences on the overall economic and other activities of the port of Šibenik. St. Anthony channel is the only entrance into the central Dalmatian town of Šibenik and international port of Šibenik. Because of its characteristics, the length which is not in a straight line, it is necessary to frequently change course at shorter intervals. Channel width in some places is reduced and causes problems, and due to sea currents and occasional strong winds, the channel is from the point of safe navigation very difficult for navigation. Moreover, passing ships over 50 GT within the channel is dangerous. The possibility of a collision, grounding and / or sinking of large ships during transit through the St. Anthony channel may eventually lead to a complete closure of maritime passenger and freight traffic to the port of Šibenik. It is therefore of utmost importance to assess the threat of a collision, grounding and / or sinking of ships in the waters of the port of Šibenik. [ABSTRACT FROM AUTHOR]

Human factors play an important role in the origin of accidents,and it is commonly claimed that between seventy andninety-five percent of industrial and transport accidents involvehuman factors, see Figure 1.Some authorities, however, claim that ultimately, all accidentsinvolve human factors.