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ÖgeTanker gemilerinde tehlikeli yük operasyonları üzerine bir risk değerlendirmesi yaklaşımı(Lisansüstü Eğitim Enstitüsü, 2025-02-07)Deniz yolu ile sıvı yük taşımacılığı, geçtiğimiz yüzyılda petrolün stratejik öneminin artmasıyla önem kazanan, zaman içerisinde ise petrol, petrol türevi ürünler, kimyasallar ve gıda maddeleri olmak üzere yüzlerce çeşit hammadde veya ürünün kıtalar arası tedarikini sağlayan büyük ve köklü endüstrilerden biridir. Her yıl milyarlarca ton sıvı yükün taşımacılığı tanker gemileri aracılığıyla sağlanabilmekte iken, bu süreçlerin devamlılığının tankerlerin emniyetli işletilmesine bağlı olması son derece açık ve anlaşılabilir bir durumdur. Tanker gemilerinin dahil olduğu özellikle petrol sızıntıları gibi büyük kazalar bu gemilerin çevreye olan potansiyel yıkıcı etkilerinin fark edilmesine ve bazı uluslararası düzenlemelerin getirilmesine neden olmuştur. İnsan yaşamını ve denizel çevreyi tehdit eden birçok uygulama ve operasyonel süreç için başta SOLAS ve MARPOL konvansiyonları olmak üzere birçok kural yürürlüğe girmiş olup, bu kurallar günümüzde halen çeşitli kodlar ve yönetmelikler vasıtasıyla güncellenmeye devam etmektedir. Bu kurallar ile uyumlu olarak günümüzde modern tankerler emniyetli ve çevreye duyarlı bir şekilde işletimin sağlanması için çift cidar yapısı, radar, elektronik seyir sistemleri gibi teknolojileri ile donatılmaktadır. Bu ilerlemeler gemi operasyonlarında personele kolaylıklar sağlamasına rağmen, çeşitli riskler nedeniyle operasyonel süreçlerde istenmeyen olaylar veya kazalar meydana gelmeye devam etmektedir. Özellikle taşınan yüklerin özellikleri dikkate alındığında tanker gemilerinin yük işlemlerinin çeşitli riskler barındırdığı anlaşılmaktadır. Risklerin fark edilemediği veya tanımlanan riskler için uygun önlemler alınmadığı koşullarda patlama ve yangın gibi son derece tehlikeli kazalar meydana gelebilir. Buna bağlı olarak ise çevre kirliliği, ekonomik kayıp ve insan hayatına yönelik tehditler gibi sonuçlarla karşılaşılabilir. Denizcilik sektöründe kaza raporları incelendiğinde patlama ve yangının bütün gemi tipleri için yaşanabilecek en ağır kazalar arasında olduğu açıkça görülür. Bu kaza riskleri esasında tankerlerde her yanıcı yük elleçlemesinde mevcuttur ve operasyonların her aşaması düşünülerek risklerin değerlendirilmesi gerektirmektedir. Ancak bazı yüklerin tehlikelilik özelliği yanıcılıkla sınırlı kalmamaktadır. İnert gaz operasyonu gibi son derece emniyetli tedbirlerin yetersiz kalabildiği hatta yükün türüne göre inert gaz uygulanmaması gerekebilen, bunun yerine özel gereksinimleri olabilen yükler de mevcuttur. Bu özel durumlar genellikle reaktif yükler için geçerlidir. Reaktivite tehlikesi olan yüklerin operasyonel süreçlerinin iyi planlanması ve çeşitlenen risklere dikkat edilmesi can, mal ve çevre açısından kritik önem taşımaktadır. Dolayısıyla bu tez çalışmasında tankerlerde söz konusu tehlikeli yük operasyonları üzerine bir risk değerlendirmesi çalışması yürütülmüştür. Tezde tehlikeli yük kategorileri arasından reaktiflik özelliği olan yükler üzerine bir inceleme yapılmıştır. Reaksiyon bir maddenin su, hava, bir başka yük ile arasında olabildiği gibi, kendi kendine reaksiyona girebilen yükler de mevcuttur. Kendi kendine reaksiyon genellikle polimerleşme olarak da bilinir ve bu tür yüklerin inhibitör adı verilen katkılar eşliğinde taşınması oldukça önemlidir. Yükün inhibitörlü operasyonu özünde bir emniyet önlemi olarak uygulansa da bu süreç de kendi içerisinde farklı tehlikeler barındırmaktadır. Yükleme öncesinde, yükleme esnasında, açık deniz seyri esnasında, tahliyede ve tank temizliği aşamalarında kazalara neden olabilecek potansiyel birçok risk faktörü olabilmektedir. Bu tezde söz konusu özel gereksinimli operasyonlar odaklanılan konu olarak belirlenmiştir. Günümüzde kimya ve plastik endüstrilerinde kullanılan önemli bir hammadde olan ve deniz taşımacılığında inhibitörlü bir biçimde elleçlenmesi gereken stiren monomer örnek tehlikeli yük olarak seçilmiştir. Tezde literatürde risk analizi çalışmalarında sıklıkla kullanılan Hata Türü, Etkileri ve Kritiklik Analizi (FMECA) esas yöntem olarak belirlenmiş, yöntem ayrıca belirsizlik ve öznel yargıların analizi konusunda başarılı sonuçlar ortaya koyan bulanık mantık yaklaşımıyla desteklenmiştir. FMECA'nın ilk aşaması olan hataların tanımlanması için öncelikle stiren monomer yüküne dair bir literatür araştırması yapılmıştır. Temel özelliklerine dair bilgiler çoğunlukla kimya alanındaki çalışmalardan ve endüstriyel kaynaklardan edinilirken, bu yükün operasyonel tehlikelerinin anlaşılabilmesi için de ilgili kaza raporları incelenmiştir. Uygulamada ayrıca söz konusu tehlikeli ve özel gereksinimli yük operasyonlarına dair bilgi ve tecrübe sahibi altı denizcilik uzmanın görüşlerinden destek alınmıştır. Hataların tanımlanması aşamasını takiben, uzmanlar toplamda 24 hata türünü meydana gelme, şiddet ve tespit edilebilirlik risk parametreleri açısından değerlendirmişlerdir. Bu üç parametre sayesinde FMECA yöntemi bir risk öncelik sayısı (RPN) değeri hesaplamaktadır. Ancak FMECA'nın klasik yaklaşımındaki bu işlemin, parametrelerin eşit önemde kabul edilmesi, parametrelerdeki küçük değişimlerin RPN'e uygunsuz bir biçimde yansıması gibi kısıtları sebebiyle iyileştirilmesi gerektiğine kadar verilmiştir. Bu noktada bulanık mantığın bir yaklaşımı olan kural tabanlı bir çıkarım sistemi modellemesi yapılmıştır. Model üç girdi değişkeni (risk parametreleri), bir çıktı değişkeni (RPN değeri) ve 125 adet kural ile hazırlanmıştır. Değişkenlerin bulanık ortamda işlenebilmesi için literatürde risk analizi çalışmalarında rasyonel sonuçlar elde ettiği görülen Gauss üyelik fonksiyonları kullanılmıştır. Bu yöntem entegrasyonunun en önemli aşamalarından ve tez çalışmasının özgün yanlarından biri kural yapısının oluşturulmasıdır. Kurallar EĞER-O ZAMAN biçim formunda ve tezin konusuna uygun bir biçimde uzmanların görüşleri alınarak hazırlanmıştır. Bu sayede operasyonel risklerin daha güvenilir bir biçimde hesaplanması sağlanmıştır. Model kurulduktan sonra uzman değerlendirmeleri modelde işlenerek her bir hata türü için bulanık RPN (F-RPN) değerleri hesaplanmış ve risklerin önceliklendirmesi yapılmıştır. Analiz sonucunda stiren monomer için inhibitörlü yük operasyonları sürecinde en yüksek risk öncelik değerli hata türlerinin i 6.78 F-RPN değeri ile "İnhibitörün miktar bakımından yetersizliği", 6.71 ile "Kargo tankı içerisinde geçmiş yük/tank temizliği maddesi kalıntısı", 6.30 değeri ile "İnhibitörün stiren monomer içerisinde homojen olmayan dağılımı" ve 6.28'lik F-RPN değeri ile "Stiren monomerin ısıtmalı bir tanka bitişik veya yakın bir tanka yüklenmesi" olduğu görülmüştür. Her bir hata türünün neden, etki ve sonuç ilişkileri incelenmiştir. Tezde ayrıca en yüksek risk öncelik değerli hata türleri için uygulanabilecek risk kontrol seçenekleri sunulmuştur. Çalışma denizcilik endüstrisine reaktif yükler ve inhibitörlü yük operasyonları konusunda uygulanabilecek örnek bir risk değerlendirme yaklaşımı sunmaktadır. Risklerin analizi için oluşturulan modelin kural yapısı, söz konusu operasyonların tehlikesini yansıtabilmesi açısından konuya uygun bir biçimde oluşturulmuştur. Parametrelerin göreceli kombinasyonlarını ve özellikle şiddet parametresinin risk üzerindeki etkisini dikkate aldığı için, önemli risklerin göz ardı edilmemesi ve risklerin doğru bir şekilde önceliklendirilmesi mümkün olmuştur. Nitekim bu durum emniyet tedbirlerinin uygulanması konusunda zaman ve finansal kaynakların da etkili kullanımına katkı sağlayacaktır. Oluşturulan model diğer tehlikeli yüklerin risk analizi çalışmalarında da gerçekçi ve kullanışlı sonuçlar verebilecektir. Buna ek olarak tez çalışmasının tankerlerde tehlikeli ve özel gereksinimli yük operasyonları konusunda literatüre önemli bir katkı sağlaması, okuyuculara operasyonel riskler ve emniyet konusunda yararlı bakış açıları sağlaması hedeflenmektedir.
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ÖgeDeep learning-based behavior analysis of seafarers(Graduate School, 2022-11-28)Human error (HE) in maritime accidents is the focus of many researches. Researchers develop many approaches to mitigate it. Apart from the approaches introduced in the literature, a new approach is proposed in this thesis. The idea is that errors are hidden in human behaviours. If the behaviours causing marine accidents can be detected, relevant warnings and improvements can be arranged to eliminate those behaviours. In this context, this thesis aims to develop a deep learning-based algorithm to analyze the behaviour of seafarers. While the frequency of maritime accidents has been in decline thanks to the studies, one single incident can have catastrophic and long-term consequences for the marine environment. Especially collisions and groundings have the potential to cause those catastrophic results for the marine environment, so the thesis is delimited to eliminate the cause of collisions and groundings. According to the literature, errors causing collisions and groundings have occurred on the bridge where the main actor involved has been the watchkeeping personnel (WP). To validate the literature and find the main reasons causing collisions and groundings, a totally of 94 maritime incident reports on collisions and groundings are obtained from the UK's Marine Accident Investigation Branch, the Transportation Safety Board of Canada and the National Transportation Safety Board of the United States of America. TRACEr-MAR taxonomy is utilized on those incident reports to find root causes of the human errors causing collisions and groundings. Results show that 74 % of the errors are directly related to the watchkeeping behaviour of the WPs. Monitoring and assessing the behaviours of WPs all the time during navigation watch has the potential to mitigate those errors. An alerting algorithm can be adjusted to warn the master or assigned officer based on behaviours causing errors gathered from monitoring results. Besides, the assessment system encourages the WPs to keep standard watch because of knowing that they are continuously being monitored and evaluated. In this thesis, a Bridge Navigation Watch Monitoring System (BNWMS) is suggested to achieve those monitoring and assessment tasks. The proposed architecture for BNWMS enables to train a model that continuously analyses the behaviour of WPs. Motion heatmap of 3D body poses over a specific time interval is suggested as an input. 2D body poses belonging to the same person are estimated from multiple camera views by using a deep learning-based pose estimation algorithm. Those estimated 2D poses are projected into 3D space by utilizing multiple-view computer vision techniques. Finally, the obtained 3D poses are plotted on the bird's-eye view bridge plan to calculate a heatmap of body motions capturing temporal as well as spatial information. After validating the proposed vision-based approach in the pilot study, the multi-view video camera system is established on an actual bridge of a commercial bulk carrier by Veysel GOKCEK to collect relevant data. 14400 motion heatmaps, each of them presenting unique 12 minutes during navigation watch, are generated from collected data. Watchkeeping behaviours of the WPs based on generated heatmaps are classified as "Not Acceptable", "Below Standard", and "Standard". Training of models is conducted by using labelled 14400 motion heatmaps. Design of 6 custom CNNs and fine-tuning of 4 pre-trained CNNs are carried out to compare different CNN architectures. Pre-trained models show a higher value than custom CNNs, owing to their pre-trained initial layers which boost feature extraction. Pre-trained VGG16 model which has the highest accuracy of 0.96 among all models is utilized to predict instant monitoring and cumulative assessments of three navigation watch based on defined classes. Numerical scores are assigned to the classes, 0 points for "Not Acceptable", 50 points for "Below Standard", and 100 points for "Standard". Both instant monitoring and cumulative assessment using numerical scores are plotted on the graph to display the performance of the watches. While instant monitoring succeeds to show the momentary condition of the navigation watch, cumulative assessment achieves to separate watches based on their performance values. The BNWMS which is consist of both instant monitoring and cumulative assessment can also produce the numerical performance of navigation on a daily, weekly, monthly or a defined period basis. An alerting algorithm can be adjusted to warn the master or assigned officer when the instant monitoring or assessment value is under the threshold. Defining the relevant threshold value based on the condition of the voyage is the feature work including revision of maritime regulations, risk assessments and company procedures. This is the first research of deep learning-based behaviour analysis on WPs keeping watch on the ship's bridge. The developed BNWMS in the thesis has introduced two new approaches to the literature. One of them is explaining the behaviour of workers by a generation of their motion heatmaps on the 2D plan of the working area within a defined period. The second one is the instant and cumulative assessment of those heatmaps by deep learning-based artificial intelligence all the time. This research will be the basis for a series of other studies. Developed novel approaches will pave the way for behaviour analysis in environments other than ships such as factories that require working in a large area.
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ÖgeAssociation rule mining for identifying factors in dynamic positioning incidents and accidents(Graduate School, 2024-01-17)Dynamic Positioning (DP) systems play a pivotal role in the offshore industry, automating vessel position and heading control. This study investigates thoroughly into the causes of DP incidents using association rule mining, specifically the Apriori algorithm within the Weka program, developed by the University of Waikato. The dataset, drawn from the International Maritime Contractors' Association (IMCA) database spanning 2004 to 2021, encompasses 691 DP incidents, providing a robust foundation for analysis. Dynamic Positioning is hailed as a technological boon in the offshore industry, ensuring vessels maintain specific positions and headings automatically. Despite its advantages, the need for vigilant monitoring persists, managed by Dynamic Positioning Operators (DPOs). This study aims to reveal the causes of DP incidents through association rule mining, offering a comprehension into the factors contributing to these incidents. The DP control system relies on data from gyro compasses and position reference systems (PRS) to maintain a vessel's heading and position. During DP operations, the setpoints for the ship's heading and position are defined by the DPO and then processed by the dynamic positioning control system, which provides control signals for both the thrusters and main propulsion systems of the vessel. The DP control system consistently aims to distribute the most efficient thrust to each of its propulsion units that are currently operational. The heading or position measurements are compared to the predicted data generated by the mathematical model. Subsequently, the variations are applied to modify the model. DP incidents, categorized into loss of redundancy and loss of position, pose significant risks, and understanding their causes is imperative for the industry's safety. Loss of position incidents can also be divided into two different types: drift-off and drive-off. While numerous studies investigate the causes of marine accidents, a noticeable gap exists in understanding the factors contributing specifically to DP incidents. This research aims to address this gap by employing association rule mining to identify patterns and relationships within a dataset comprising 1352 incidents from the IMCA database. The Apriori algorithm, integrated into the Weka program, was employed for association rule mining. Weka, a machine-learning workbench, provided a user-friendly interface for dataset analysis. The initial dataset was filtered to 691 incidents for in-depth exploration. The dataset's origin from the IMCA, a reputable source in the maritime offshore industry, enhances the study's credibility. Analysis reveals primary causes of DP incidents, with thrusters and propulsion failures (24%), computer failures (15%), power-related failures (14%), PRS-related failures (13%), and human factor-related failures (9%) dominating. Secondary causes, notably human factors (54.5%) and electrical problems (13.5%), exhibited an upward trend from 2017 to 2021. In-depth association rule analysis provides insights into the triggers and outcomes of DP incidents. Accidentally pressing buttons (55.2%) and pressing wrong (standby) buttons (13.1%) emerged as significant triggers. Environmental factors, particularly squalls, swells, and waves, were strongly associated with drift-off incidents (100%). Specific causes, such as power-related failures, thruster or propulsion failures, and PRS-related failures, exhibited distinct outcomes, contributing to a nuanced understanding of DP incidents. Based on findings, recommendations focus on enhancing DPO training and industry practices. Suggestions include extending the duration of DP simulator courses, revising sea time requirements, and introducing specialized certifications for each vessel types. Simulator-based training should encompass specific actions during emergencies, blackout recovery, and responses to environmental changes. Extending DP Simulator Courses: Recognizing the significance of practical knowledge, it is recommended to increase the duration of DP simulator courses. This extended training period allows trainee DPOs to gain hands-on experience in a simulated environment, comparing real-life and simulator incidents. Revising Seatime Requirements: Current sea time requirements post DP simulator course completion may be insufficient for gaining the necessary practical experience. Policymakers are encouraged to consider extending the DP sea time duration (currently set at 2 hours for each DP sea time day) or extending the mandated overall sea time, currently set at 60 days, providing prospective DPOs with more hands-on opportunities. Moreover, it is not recommended to transfer any surplus active sea time days acquired prior to the DP simulator course (Phase 3) to subsequent phase (Phase 4). Specialized Certification: A specialized certification program is proposed, wherein DPOs gain certification based on their experience with specific vessel types. This approach ensures that DPOs possess vessel-specific knowledge before assuming responsibilities, enhancing overall competency. Industry stakeholders are encouraged to develop expert specialization programs, allowing DPOs with extensive experience on specific types of vessels to obtain specialized certificates, ensuring they possess the requisite knowledge for their roles. Simulator-Based Emergency Training: DPOs are recommended to undergo simulator-based emergency training covering various scenarios, such as blackout recovery, response to environmental changes, and actions during emergencies. This ensures DPOs are well-prepared for critical situations. Meteorology Training: Given the association of environmental factors with drift-off incidents, meteorology training combined with DP operations is recommended. This training should include actions during not only emergencies, but also extreme weather conditions such as squalls, solitons, unpredictable changes in current, etc. Additionally, maintaining continuous and vigilant watchkeeping is of significant importance for ensuring the safety of operations. Power-related Training: Considering the significant impact of power-related failures, blackout recovery training is highlighted as crucial. DPOs, EROs, ETOs, should be well-versed in blackout recovery processes and vessel-specific characteristics. Additionally, it is recommended to provide comprehensive training for DP key personnel before they embark on their vessel assignment. This training should encompass ship-specific thruster/propulsion training, as well as power management training, covering the concept of different propulsion systems, their limitations, and blackout prevention and recovery processes. Home Training Modules: DP vessel managers and operators should implement home training modules for personnel engaged in DP operations. These modules, covering significant documents related to the vessel and its operations, enhance familiarity and competence. These documents encompass a variety of materials, including, but not limited to, the most recent FMEA and DP annual trial reports of the joining vessel, the DP operation manual, DP incident or failure reports (if any), the latest DP audit report, WSOG, TAM, CAMO, etc. Human Factor Analysis: A more detailed analysis of human factors and situational awareness is recommended for future research. Understanding the intricacies of human error and awareness can contribute to minimizing risks associated with DP incidents. Competency Factor Analysis: For future studies, competency factors of DPO for different types of DP vessels can also be identified in more detail. By means of the work, the competency factors can be achieved by taking a specific vessel type or incident model and employing advanced approaches such as machine learning or Bayesian network analysis, among others. Situational Awareness: Further studies should also explore human factors and situational awareness in greater detail. Situational awareness is vital for DPOs during emergencies, and detailed analysis can contribute to better decision-making and risk mitigation. This condensed summary provides a detailed overview of the original thesis, retaining the essence of the research while presenting key findings and recommendations. Understanding the causes of DP incidents is crucial for enhancing safety in the offshore industry. The comprehensive recommendations aim to guide industry stakeholders, DP vessel managers and operators, training centers, and policymakers in implementing effective measures to reduce the occurrence of DP incidents and improve overall safety standards. The implications of this research extend beyond the academic realm, addressing the substantial financial stakes in the offshore industry. By identifying causes and providing targeted recommendations, this study offers a practical guide for industry practitioners. Potential directions for future research include detailed competency factors, advanced analytical approaches, and in-depth analyses of human factors and situational awareness. In conclusion, this study illuminates the complexities of DP incidents, providing a comprehensive understanding of causes and recommending measures to enhance safety. Given the substantial financial implications of incidents in the offshore industry, this research contributes to a proactive approach, allowing stakeholders to anticipate and address risks effectively.
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ÖgeA psychological assessment model on the commercial maritime transport sector(Graduate School, 2024-02-07)The intricate tapestry of maritime psychology unfolds within the vast expanse of the seas, where over a million seafarers navigate under unique and demanding circumstances. This comprehensive exploration traverses the currents of evolving methodologies, challenges, and trends within the maritime psychology landscape. Anchored in a profound literature review, the study illuminates the tangible challenges faced by seafarers, from isolation and hierarchical structures to harsh conditions and prolonged separation. The maritime industry's sheer scale, combined with the distinct challenges of maritime life, underscores the profound implications for seafarers' mental well-being. In tandem with advancing technology, the study delves into the integration of machine learning and artificial intelligence in psychological assessments, sparking debates on diagnostic criteria, expert opinions, and ethical considerations. This application becomes particularly critical in an industry where traditional support systems are not only physically distant but also lack adequate medical facilities. The study unfolds through a bibliometric analysis, revealing a surge in research activity post-2010, with the highest publication rates in 2021 and 2022. The disruptive impact of the COVID-19 pandemic on seafarers' lives and mental health emerges as a significant catalyst for this increase. Moving beyond theoretical frameworks, the investigation encompasses four thematic clusters: "Research Design," "Spatial Design," "Data Collection Tools," and "Assessment Approaches." Observational studies take precedence, emphasizing the importance of understanding naturally occurring events and relationships in maritime contexts. Spatial design assumes critical importance, distinguishing studies in simulated environments from those in real-life maritime settings. The diverse array of data collection tools, from surveys and questionnaires to interviews and simulator data, reflects the multifaceted nature of maritime psychology. A paradigm shift is evident in assessment approaches, with "Statistical Analysis," "Machine Learning," and "Statement Analysis" taking center stage. The practical application centers around depression, a prominent psychiatric condition affecting seafarers. Leveraging the Beck Depression Inventory-II (BDI-II), a dataset of 746 records is obtained. Fuzzy logic and the Adaptive Neuro-Fuzzy Inference System (ANFIS) methodology, integrated with MATLAB Fuzzy Logic Toolbox, provide a seamless fusion for assessing depression severity. The clustering phase adopts both psychiatric and mathematical approaches, resulting in four distinct clustering groups. The pivotal outcome underscores the high accuracy achievable in predicting depression severity through a machine learning-based approach. The ANFIS model tailored for 2-factor clustering consistently outperforms its 5-factor clustering counterpart. The mathematical approach, specifically the 3-factor clustering, emerges as the more effective choice, highlighting the need for nuanced comprehension of psychiatric factors. The ANFIS model's performance details reveal minimal training RMSE, checking RMSE, and high R 2 scores, emphasizing its efficacy in providing nuanced insights into seafarers' mental well-being. The study navigates ethical considerations associated with data collection, advocating for the necessity of developing culturally sensitive measurement tools. Fuzzy logic, specifically ANFIS, emerges as a vital tool in deciphering complex datasets, promising to revolutionize mental health assessments in the maritime industry. While the study acknowledges limitations and the need for future research with more extensive samples, it contributes significantly to maritime psychology methodologies. In conclusion, this research voyage extends beyond theoretical frameworks, offering a practical tool for assessing and addressing the psychological challenges faced by seafarers. The success of the ANFIS model underscores its potential in fostering a healthier and safer maritime working environment. The study advocates for investments in machine learning-based systems, supported by self-sustaining servers, to enhance mental health services in the maritime sector, charting a course towards a more resilient and supportive maritime industry.
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ÖgeA conceptual approach for design and development of serious games in maritime domain(Graduate School, 2024-01-19)Around 80,000 merchant vessels which are manned by about 1.6 million seafarers transport around 90 percent of world trading products. Transportation of important products such as food and medical supply during the Covid-19 pandemic has even spotlighted the vital role of seafarers. International Convention on Standards of Training, Certification and Watchkeeping (STCW) (referred as STCW in this study), which has been issued by International Maritime Organization (IMO), defines the training and competency standards of the seafarers. It has been argued that there is an important gap between required on-board competency levels of seafarers and their actual levels of competency. Despite the regulatory and technological advancements in shipping, human error still plays a major role in more than 80% of shipping accidents. For enhanced competency development of seafarers, maritime industry needs to find cheaper, accessible and more flexible methods of practical education and training. Serious gaming, as a technology-enabled instructional method, offers an important potential for maritime domain as it provides interactive and authentic learning environments. In this regard, main objective of this study is proposing a holistic conceptual approach for effective design, development and utilization of serious games in maritime domain. More specifically, this study intends to provide the academicians and practitioners with a foundational basis for creating and using maritime serious games. For this purpose, a systematic literature review on serious game design approaches with a special focus on future skill development is firstly conducted. In this review, 32 serious game design models which provide practical steps for serious game design are selected. It is found that 8 (25%) of these design approaches support at least one future skills, among which problem‐ solving as well as collaboration and teamwork are the most commonly supported ones. It is also discovered that clear goals and interactivity, used in 6 (75%) and 5 (63%) of the 8 design approaches respectively, are the most commonly implemented game design elements. Considering the significant literature gap on the implementation of serious games for future skills development, this literature review consequently provides valuable insights for the game designers, software developers, educational technology researchers, and engineering educators in various domains. After that, Serious Game Design for Maritime (SGDM), a holistic model to support the design of maritime serious games is proposed. Using the SGDM model, MARITIME LEADERS at SEA (ML@S), a 3D serious game to enhance the leadership and teamwork skills of young seafarers and maritime students, has been prototyped. ML@S game is conceptualized as a module of the "Maritime Gamentor" platform. Using the SGDM model, TASK-BASED RISK ASSESSMENT AT SEA (MRA@S) game is also designed and prototyped for task-based risk assessment training in preparation for the Ship Inspection Report (SIRE 2.0) inspections of Oil Companies International Marine Forum (OCIMF). Proposed model (SGDM) as well as the explained methodology can be followed by technology initiatives, game designers, and researchers for development of similar maritime serious game modules on soft skills and technical skills. Besides, functions of the Maritime Gamentor platform can be further extended in the maritime domain by adding similar serious game based training modules. After prototyping the games, an experimental study was conducted for analyzing the efficiency of the ML@S game and proposed SGDM model. It can be concluded from the experimental study that the game was tested successful by the students in all 4 categories (motivation and engagement, game effectiveness, game clearness, future use). This being the case, it can be put forward that the developed ML@S game can be used as a means of leadership education and training. Looking at the broader picture in the study, it was proved that the SGDM model can be practically applied to design successful maritime serious games. In sum, serious games can provide maritime students and young seafarers with the practical education and training they need in a cost effective way. For this reason, it is believed that the proposed approach can be followed by technology initiatives, game designers, and researchers for development of similar maritime serious game modules on soft skills and technical skills. Consequently, this research intents to contribute to safety, security and environmental protection in maritime domain by providing an insight into enhanced competency development.