## 3 AI Implementations in Maritime Transport Systems
### AI in Risk Analysis and Management  
Artificial intelligence (AI) has revolutionized the field of risk identification in maritime transportation by leveraging advanced data analysis and machine learning (ML) techniques. Traditionally, risk identification in maritime operations relied on historical data, expert judgment, and manual inspections. However, these methods often fall short of capturing the dynamic and complex nature of maritime risks [11; 17]. AI systems can process vast amounts of data from various sources, including vessel tracking systems, weather forecasts, maintenance records, and incident reports. By analyzing these data, AI algorithms can identify patterns and correlations that may indicate potential hazards. For instance, machine learning models can detect anomalies in vessel movements, which might suggest mechanical issues or navigational errors. Additionally, AI can assess environmental conditions, such as sea state and weather patterns, to predict hazardous situations like rough seas or storms [18; 19]. Furthermore, AI enhances situational awareness by integrating real-time data from multiple sensors and information systems. This comprehensive data analysis allows for the early detection of risks, enabling timely interventions to prevent accidents. For example, AI-powered systems can monitor engine performance and alert operators to potential failures before they lead to critical incidents [13; 20]. Predictive analytics, a key application of AI in maritime risk management, involves using historical data and machine learning models to forecast future risks and their potential impact. By predicting the likelihood of various risk scenarios, maritime operators can proactively implement preventive measures and mitigate potential hazards. One of the primary benefits of predictive analytics in maritime transportation is its ability to enhance decision-making. AI models can evaluate numerous risk factors simultaneously, providing a more comprehensive risk assessment than traditional methods. For example, predictive models can analyze factors such as vessel age, maintenance history, route characteristics, and crew experience to estimate the probability of mechanical failures or accidents [14; 21]. Moreover, predictive analytics can optimize maintenance schedules by predicting equipment failures before they occur. This approach, known as predictive maintenance, helps reduce unplanned downtime, lowers maintenance costs, and improves overall vessel reliability. AI algorithms can analyze sensor data from critical machinery to detect early signs of wear and tear, enabling timely repairs and replacements. AI systems process vast amounts of data from various sources, including vessel tracking systems, weather forecasts, maintenance records, incident reports, and real-time sensor data from onboard systems monitoring engine performance, hull stress, and other critical parameters. Several AI algorithms and methods are employed to analyze these data. Neural networks, for instance, are used for pattern recognition and anomaly detection. They can identify unusual vessel movements, indicating potential mechanical issues or navigational errors. Decision trees are utilized for classification and decision-making, helping to predict the likelihood of specific incidents based on historical data, such as assessing the risk of collision based on vessel speed, direction, and proximity to other ships. Support vector machines (SVMs) are employed for classification and regression analysis, predicting outcomes like potential equipment failures by analyzing patterns in sensor data. Bayesian networks, as probabilistic models, estimate the likelihood of various risk scenarios by combining different data sources. For example, they can predict the probability of adverse weather conditions affecting navigation safety. Cluster analysis, used for grouping similar data points, helps identify patterns and trends in large datasets, such as clustering vessel routes to highlight common risk areas requiring enhanced monitoring.