Ship Accidents Research Articles

Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm and TOPSIS method

The busy maritime traffic and occurrence of ship accidents have led to a growing recognition of the necessity to maritime emergency resources allocation. The port emergency resource allocation is of significant importance for the maritime safety. This paper presents an optimized allocation model for port emergency resources based on the improved multi-objective particle swarm optimization (IMOPSO). The model introduces the crowding distance and improves the external archive update strategy. The particle inertia weight is adjusted and a dynamic mutation operator is incorporated. The entropy-weighted technique for order preference by similarity to an ideal solution method is also employed to identify the optimal solution. A comprehensive comparison with MOPSO has been presented and discussed. Three metrics of generational distance (GD), spacing (SP) and delta indicator (Δ) were employed for performance evaluation. The results demonstrated that the proposed IMOPSO algorithm exhibited superior performance and robustness, with average values of GD = 0.0386, SP = 0.0023 and Δ = 0.6468 for ZDT test functions. The model efficacy is further validated by a case study of oil spill dispersant configuration at Zhanjiang Port, China. Seven alternative schemes have been obtained, among which the optimal scheme is selected by the entropy-weighted TOPSIS method. The overall cost is potentially to be reduced by approximately 33.03 %. The present study would provide a reference for the water pollutant control and environmental management in port waters.

Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety

The fishing industry has a large role in the Indonesian economy, with potential profits in 2020 of around US$ 1.338 billion. Tuban Regency is one of the regions in East Java that contributes to the fisheries sector. Fisheries relate to the work of fishermen. Accidents in shipping are still a major concern. One of the natural factors that influence shipping accidents is the height of the waves. Fisherman safety regulations have been established by the Ministry of Maritime Affairs and Fisheries and the Meteorology, Climatology and Geophysics Agency. Apart from regulations, the results of wave height predictions using the Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) methods can help fishermen determine shipping departures, thereby reducing the risk of accidents. In this study, the Grid Search hyperparameter tuning process was used for both methods which were carried out on four location coordinates. Based on the analysis results, LSTM is superior in predicting wave height for the next 30 days because it can predict wave height at all three locations, with results at the first location (RMSE 0.045; MAE 0.029; MAPE 8.671%), second location (RMSE 0.051; MAE 0.035; MAPE 10.64%), and third location (RMSE 0.044; MAE 0.027; MAPE 7.773%), while XGBoost only has the best value at fourth location (RMSE 0.040; MAE 0.025; MAPE 7.286%).Hyperparameter tuning with gridsearch is used in LSTM and XGBoost to obtain optimal accuracyLSTM outperforms in three locations, while XGBoost outperforms in the fourth location.Advanced prediction techniques such as LSTM and XGBoost improve fishermen's safety by providing accurate wave height estimates, thereby reducing the possibility of shipping accidents.

Data-driven Bayesian risk assessment of factors influencing the severity of marine accidents in port areas

The most prevalent types of ship accidents in port areas are allisions, collisions, and groundings. A comprehensive risk assessment is needed to prevent and mitigate these accidents and their consequences. This study evaluates the risk of such accidents in port areas by presenting a model that elucidates the relationship between risk-identifying factors (RIFs) and accident severity. In this context, the RIFs are determined by analyszing the reports of 528 accidents that occurred in port areas between 1995 and 2023. Subsequently, the model is created by analysing the data derived from these reports using the Tree Augmented Naive Bayes (TAN) algorithm, which is an approach of the data-driven Bayesian network method. The findings of the study indicate that accident type, wind, ship age, and vessel type are the most influential factors in predicting the severity of accidents in port areas. It is thought that the model will assist port authorities in identifying operational risks contributing to accidents and in formulating preventive regulations.

Implementation of a semi-automatic approach for detection and extraction of oil slicks using Sentinel-1: Case study of the Moroccan exclusive economic zone

Due to its strategic geographic location, Morocco experiences heavy ship traffic, which inevitably has consequences. Ship accidents and illegal discharges, commonly referred to as oil spills, are frequent and pose significant harm to the marine ecosystem. Therefore, employing new technologies, such as satellite monitoring, is crucial and will greatly enhance the ability to monitor and assess these oil spills. The present work is based on Sentinel-1 SAR images and aims to take into evidence the extent of the hydrocarbon marine pollution in all of the Moroccan EEZ. More than 220 possible cases were detected and mapped during the last decade (2014–2024). A batch process was developed to automate the detection of oil spills by identifying areas with reduced Normalized Radar Cross Section (NRCS) values. This process incorporates corrections, masks, wind field estimation, and oil clustering. The method successfully detected approximately 80% of the cases between Kenitra and Casablanca. The closest detected spill is located 1 km from the coast, the largest spill covers an area of 90 km2, and the most extended spill measures about 145 km. This study can help raise awareness among governmental authorities about potential environmental damage and can be used to train artificial intelligence models, which could simulate oil slick propagation.

A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents

Human factor is the main cause of maritime accidents. However, the in-depth study on the mechanism of cognitive errors in seafarers’ human error needs to be supplemented. This study empirically analyzes 116 collision cargo ships along the Yangtze River trunk line. To explore the relationship between seafarers’ task errors and the cognitive process of accidents, a seafarers’ cognitive error mining model is proposed, which integrates the Information, Decision, and Action (IDA) cognitive model into the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). According to the approach of mining from the outside to the inside, firstly, the error mode in the cognitive stage of seafarer tasks are identified. Then the fine-grained main manifestation of the task errors is mined by K-means, and Apriori algorithm is employed to mine the hidden features between the external and internal error modes. The results show that there are four main problems of Yangtze River ship accidents, and seafarers have four to seven corresponding causal chains from psychological error mechanism to performance shaping factors for each major issue in the cognitive stage. On the basis of the results, this study summarizes the guiding principles of accident prevention and seafarer management measures to provide decision support for water traffic safety.

Maritime transportation risk assessment: A multilevel node relationship-based fuzzy bayesian network

Maritime transportation safety has encountered unprecedented challenges arising from abrupt changes in various risk attributes in recent years, including natural weather and non-traditional security threats. This study presents a novel two-phase framework for assessing the maritime transportation risk. Firstly, a fuzzy Bayesian network considering multi-level node relationships was constructed to more accurately assess the transportation risk in various sea areas. In the second phase, RF-Shapley was developed to identify crucial risk nodes that exert substantial influence on transportation risk within individual sea areas. The results showed that transportation risk was the lowest in the South Asian and the highest in the Middle East waters. Critical risk nodes were different for each sea area, such as pirate attack node in Southeast Asian waters, extreme weather node in Northeast Asia, changing these nodes would be more helpful in improving transportation safety. Moreover, the contribution of critical risk nodes exhibited variations, such as combating terrorism and piracy were more conducive to reducing transportation risk in Middle Eastern waters, while ship accidents node be low contribution. These results offer valuable insights for evaluating transportation risk in uncertain environments and can serve as a reference for stakeholders to devise tailored policies and action plans.

Shipping Safety Innovation: The Role of Big Data and IoT in Reducing Risk

Shipping safety is a crucial aspect in the maritime industry which faces various risks of accidents. This research aims to examine the innovative role of Big Data and the Internet of Things (IoT) in improving shipping safety through accident prediction and prevention. The research method used is qualitative using secondary data from industry reports, journal articles and related publications. The research results show that Big Data can improve the ability to predict and prevent shipping accidents by analyzing weather data, ship movements and sea conditions to produce accurate predictive models. In addition, IoT makes a specific contribution in real-time monitoring and risk mitigation through sensors and AIS systems that provide real-time data for fast and informed decision making. However, the integration of Big Data and IoT faces major challenges such as cybersecurity, implementation costs, and the need for adequate infrastructure. Nevertheless, the prospects for developing this technology are very promising with increased security protocols, reduced costs through innovation, and development of maritime telecommunications infrastructure. In conclusion, Big Data and IoT have great potential to improve shipping safety, but require attention to existing challenges to realize their full benefits.

Ship accident re-enactment based on AIS data and virtual reality: a case study of “Xiang Xiang Yin Huo 0410” ship accident

Abstract The recurrence of ship accidents was an effective method of maritime investigation. To analyze the cause of the sinking of “Xiang Xiang Yin Huo 0410”, a vessel accident recurrence simulation system was constructed by integrating an automatic identification system and virtual reality technology to replicate the navigation and accident process of the accident vessel. The voyaging process of the accident vessel was reduplicated in the simulation system according to the AIS information, such as speed, course, and turning speed. The mathematical model of accident vessel maneuvering was established by using MMG to reproduce the ship maneuvering scheme based on the maneuvering data before the accident obtained through on-the-spot investigation. The navigation and sinking process of the accident vessel were reproduced visually, and the cause of the accident was judged and analyzed intuitively by constructing a three-dimensional model of the accident vessel and a mathematical model of ocean waves. The actual application indicates that the ship’s navigation and accident process under external force, combined with the ship’s maneuvering data, automatic identification data, and hydrometeorological data, was consistent with the actual accident process of the accident. The method proposed in this paper provided a new idea for the investigation and analysis of maritime accidents.

Study of The Ruling of The Shipping Court in Burning Boat Accidents That Impact Losses Passenger as Consumers

Introduction: Passenger ship accidents frequently occur in Indonesian waters, resulting in injuries and fatalities. Unfortunately, the affected individuals only receive partial compensation from their travel insurance and are not granted full compensation under their consumer rights as outlined in Act Number 8 year of 1999 about Consumer Protection. This leaves them feeling disheartened about reclaiming their lost possessions.Purposes of the Research: The article delves into the legal rights of passengers impacted by sea transportation accidents, covering physical injury, loss of life, and property damage. The Shipping Court conducted a thorough investigation into the incident involving KM Express Cantika 77 on October 24, 2022, with a strong focus on uncovering the cause and implications.Methods of the Research: With normative juridical research methods, and based on literature data and the decision of the Shipping Court in the case of the burning of the Ferry KM Express Cantika 77 on October 24 2022 at 13.20 WITA in Naikliu Waters. By analyzing using a qualitative approach to determine the rights of passengers who got an accident.Results of the Research: The Indonesian Shipping Court decides on passenger ship accident cases only by examining the causes and impacts of the accident. It is recommended that the Shipping Court become a special court, like other special courts such as the Industrial Relations, Fisheries, and Forestry Courts. These reforms will allow the Court to examine and prosecute based on the criminal, civil, and administrative aspects of shipping incidents.

Deep Learning-Based Detection of Oil Spills in Pakistan’s Exclusive Economic Zone from January 2017 to December 2023

Oil spillages on a sea’s or an ocean’s surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan’s territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan’s Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures.

The Future of Coastal Navigation Safety in Egypt: Improving the Reliability of Electronic Charts

Abstract This paper investigates the integrity and reliability of electronic navigation charts (ENCs) used for maritime navigation along the Egyptian coast. Comprehensive assessments were conducted, focusing on critical stretches including the Suez Canal, Strait of Jubal, Aqaba Gulf entrance, and Hurghada Port access channels. The findings expose alarming accuracy gaps in these vital yet confined transit zones, with Category of Zone of Confidence (CATZOC) classification results indicating positional uncertainties exceeding five hundred meters. Such imprecision severely erodes navigational safety buffers for ships amidst intense traffic volumes. Meanwhile, proximity to delicate coral ecosystems and coastal communities raises the stakes for averting accidents. Responding to these urgent threats, promising initiatives are already underway between Egyptian agencies and international hydrographic bodies to systematically address ENC deficiencies through new surveys and data enhancements. Mandatory functionality checks, crew training, and compliance enforcement will also optimize the utilization of existing ECDIS installations. Sustained investments to upgrade this core informational infrastructure will help align the country's navigational framework with best practices for protecting life, property, and the environment. A multifaceted strategy tackling both human and technical dimensions across ships, ports, and shoreside administrators is essential for eliminating needless risk. Strengthening ENC integrity will provide Egyptian-flagged and visiting vessels with greater situational assurance when transiting confined waterways, while preventing devastating shipping accidents that could damage fragile marine ecosystems.

Mitigating the Risk of Ship Accidents with an Integrated Approach to Maritime Safety Management

This research explores an integrated approach in maritime safety management to reduce the risk of ship accidents. With the increasing volume of maritime traffic and the complexity of shipping operations, ship safety has become a critical aspect that must be considered. This study identifies and analyzes various safety technologies and strategies that have been implemented in the maritime industry, such as Automatic Identification Systems (AIS), Vessel Traffic Management (VTS), Weather Early Warning Systems, and crew training and certification programs. Data collected through literature reviews and case studies show that the application of this advanced technology significantly improves shipping safety and reduces accident incidents.

Human Factors Analysis and Classification System (HFACS) on Accidents Due to Tilting Ship

The transportation industry is the main point of the distribution of goods and passengers. Data shows the number of shipwrecks is very high. The accident was caused by many factors, one of which was the tilting of the ship. The research starts from categorizing the causes of ship accidents through existing secondary data, FGD with expert judgments to find the root of the problem and also looking for what mitigations can be done so that similar incidents do not repeat. And the analysis is made in the Human Factors Analysis and Classification System (HFACS) method. The results of this study indicate that there are human factors in recurring events at all stages of HFACS such asunsafe acts, the preconditions for unsafe acts, unsafe supervision and organisational influences. For the stages of unsafe acts are Skill based Errors, decision errors, Perceptual Errors, and Exceptional. For the stage of the preconditions for unsafe acts are technological environment, physical environment, and adverse mental state; and for the stage of unsafe supervision is the lack of supervision; for organizational influences there are resource management and organizational climate.

Normative Study On Criminal Liability Of The Captain For The Occurrence Of A Fatal Accident That Causes The Death Of A Person

The purpose of this study is to describe criminal liability for the captain for the occurrence of fatal accidents that cause the death of people. The method provided uses a data normative law research approach using secondary data and techniques with document and literature studies. The study results show that the captain is the highest leader on a ship and is responsible for the occurrence of ship accidents unless it can be proven otherwise. The captain's criminal liability for a fatal accident is caused by technical, human, and natural factors. Ship accidents caused by technical and human factors result from a lack of caution (negligence) or a mistake. Because of the fulfillment of the elements of criminal liability, namely mistakes (schuld), being able to take responsibility, and the absence of excuses, the captain is burdened with criminal liability. Meanwhile, ship accidents caused by natural factors are included in noodtoestand. This force majeure circumstance is a reason for forgiveness in criminal law so that the captain is not burdened with criminal liability.

Introduction to the Concept of the German Navy Stability Standard DMS 1030-1

Following World War II and the founding of the German Armed Forces, lessons learned from ship accidents as well as scientific advancements called for a new stability regulation specific to the Federal German Navy, the BV 103. While certain minor modifications have been made through the years since then, the basic concept behind this standard still remains effective up to the succeeding regulation DMS 1030-1 of this day. With the most recent additions it has been proven to keep up with and even outclass the safety levels of current civilian stability regulations. The key to this success is early adaptation of available scientific techniques outside the usual constraints of large regulatory bodies.

Towards an analysis framework for operational risk coupling mode: A case from MASS navigating in restricted waters

Maritime Autonomous Surface Ships (MASSs) constitute highly interconnected and tightly coupled multistate systems. Incorporating the coupling effects of both interactions and dependencies is centrally important to ensure navigation safety of MASSs. This paper proposes a framework for examining the coupling effects in the operational modes (OM) of MASSs. Failure Modes (FMs) of MASSs related to interactions with the environment and subsystems are identified using 24Model developed based on conventional ship accidents involving grounding in the west of Shenzhen port, China. FMs related to grounding are classified into the five risk types of human, organization, ship, environment, and technology, considering three OMs (states), i.e., manual control, remote control, and autonomous control. Based on this classification, the N-K model is applied to calculate the risk coupling values of scenarios involving multiple risk types. These scenarios are then ranked by the risk coupling values of the three OMs, and values are compared among different OMs. The results indicate that for grounding in the west of Shenzhen port most risk coupling values increase with increasing risk type diversity. Furthermore, the risk coupling mechanism in remote control differs significantly from that in manual control and autonomous control, thus risk management of MASS OMs should vary.

An investigation of sea state conditions at the time of ship accidents in Indonesia

ABSTRACT In this study, we investigate the sea state conditions during the two cases of ship accidents that have recently occurred in the Indonesian ocean. The first accident case occurred in the middle part of Indonesia at the end of May 2022 resulted in fatalities. Similarly, another incident was in the eastern part of Indonesia in the middle of July 2022, killing 12 people. For both cases, we observed the maximum wave height, significant wave height, and wind speed during the respective accident periods. The observations were carried out using the global ocean wave model, WAVEWATCH III, in conjunction with Altimeters and ASCAT scatterometers. Specifically, the study employed data from Cryosat-2, Jason-3, Saral, Sentinel-3B, Sentinel-6A, Metop-B and Metop-C. The resultant satellite observations are consistent with the model, meaning that the wave heights at the time of the accident were very high. The first case clearly shows that strong wind and high waves most likely triggered the accident. As a result, all stakeholders who run their ocean transport business must consider the warning from the related agency, which is, in this case meteorological, climatological, and geophysical agency. Such proactive measures have the potential to mitigate marine accidents stemming from adverse weather conditions.

Exploring the ship operation safety indicators of international ports in Taiwan

Port safety plays an important role in port operations. Ship size has become larger and the port environment has rapidly changed in recent years. Ship accidents occur in the port area due to the complex environment in the port area. To improve past decision analysis methods on port operation safety fields and reduce fatalities and financial loss for potential accidents, the novelty of this study is to construct a safety index of ports with the application of the Best Worst Method (BWM). Four dimensions and 14 indicators were summarized based on an intensive literature review. The BWM was implemented to prioritize the weights of dimensions and indicators. Based on 21 expert questionnaires, the results indicate that the ranking of dimensions is ‘human’, ‘ship facilities’, ‘port facilities’, and ‘documentation check’. Regarding the ranking of indicators, the top three are ‘fire-fighting and life-saving equipment’, ‘captain’, and ‘pilot’. Safety improvement strategies (e.g. revising inappropriate operational rules and strengthening human safety education and training) based on these research findings are provided. The merits of this paper are presenting a simpler questionnaire-filling method and overcoming the traditional complicated questionnaire survey process and research limitations (e.g. indicator independence problems in the Analytic Hierarchy Process, and the complexation of filling out a questionnaire in the Analytic Network Process). In addition, the findings can help decision-making for port management authorities, port practitioners, and shipping operators (shipowners) regarding policy implementations of port safety.

Evaluation of dynamic risk factors for ship operations

ABSTRACT The comprehension of the risk sources behind ship accidents is important not only due to environmental destruction, and loss of life and property, but also because of their economic effect on a global level. There are static risk evaluation forms on ships to decrease or prevent risks stemming from the nature of routine operations. However, ship operations are performed under the effect of static and dynamic risks. For this reason, the risks that may be caused by changing conditions must be taken into account to prevent accidents. However, the safety of operations can be maintained with self-renewing risk profiles. In the present study, dynamic risk factors that affect the safety of ships were identified and a questionnaire was prepared to evaluate the effects of these risk factors on operations. It was examined quantitatively how dynamic risk factors increase the risk of operations. Consequently, the study examines the effects of dynamic risk factors on ship operations and provides a framework for pre-accident risk management, a practical contribution to the creation of a safe and efficient working environment for the ship.

Perbandingan Skor Prediksi dalam Pembelajaran Mesin pada Keselamatan Penumpang Kapal

Passenger safety is an important aspect of the shipping industry, and precise predictions of emergencies and safety situations can provide great advantages in managing risks at sea. Precise prediction of emergency and safety situations can provide great advantages in managing risks at sea. This research involves collecting and analyzing historical data on ship accident incidents and their influencing factors. Machine learning methods, such as logistic regression, KNN, Naïve Bayes Classifier, Decision Tree, Random Forest, etc. will be used to identify patterns in the data that can be used to predict ship passenger safety. The data used in this study includes variables such as weather, ship state, number of passengers, and other relevant factors. The research data was taken from titanic passengers. The results of this study resulted in a model that is suitable for predicting the safety of ship passengers, namely the random forest model and decision tree getting the highest score compared to other models, the prediction score is 84.29%.