## 3 AI Implementations in Maritime Transport Systems
### AI in Hazardous Material Handling  
AI-powered training simulations have become essential tools in enhancing seafarer resource management (SRM) within the maritime industry. These simulations use advanced AI algorithms to create realistic and dynamic training scenarios that mimic real-world maritime operations. By exposing seafarers to a variety of operational challenges and emergency situations in a controlled environment, AI-driven simulations significantly improve their decision-making, problem-solving, and teamwork skills. AI systems have significantly advanced the real-time monitoring of hazardous materials in maritime transportation. These systems utilize a combination of sensors, data analytics, and machine learning algorithms to continuously monitor the status and condition of hazardous materials on board ships. This real-time monitoring capability ensures that any potential risks are identified and addressed promptly, thereby enhancing safety and compliance with regulatory standards [43; 44; 45]. Key features of AI-powered real-time monitoring include sensor integration, where AI systems integrate data from various sensors that monitor parameters such as temperature, pressure, humidity, and chemical composition. These sensors provide continuous data streams that AI algorithms analyze to detect anomalies and potential risks. Advanced data analytics techniques process the sensor data to identify patterns and trends that may indicate hazardous conditions. For example, an unexpected increase in temperature or pressure within a containment unit can signal a potential leak or failure. AI algorithms can also predict when maintenance is required based on the condition of the hazardous materials and the containment systems, helping to prevent incidents by ensuring that equipment is serviced before a failure occurs. Additionally, AI systems ensure that the handling and storage of hazardous materials comply with safety regulations and standards. By continuously monitoring conditions, these systems help maintain adherence to protocols and provide documentation for regulatory compliance [46; 47; 48]. Automated response systems driven by AI are crucial for managing hazardous material incidents effectively. These systems can quickly and accurately assess a situation, determine the appropriate response, and execute actions to mitigate risks. The use of AI in automated response systems enhances the speed and efficiency of emergency responses, reducing the potential impact of hazardous material incidents [49; 50; 51]. AI systems detect incidents through real-time monitoring and data analysis. When a hazardous condition is identified, the system triggers an alert and begins the response process. AI algorithms evaluate the severity of the incident and determine the best course of action, considering various factors such as the type of hazardous material, the extent of the breach, and environmental conditions. The system can automatically execute response actions, such as activating containment measures, shutting down affected systems, and notifying emergency response teams. These automated actions help to quickly control the situation and prevent the spread of hazardous materials. Additionally, AI systems facilitate communication and coordination among seafarers and emergency responders. By providing real-time information and guidance, these systems ensure that all parties are informed and can collaborate effectively during an incident [49; 50; 51]. While AI systems offer significant benefits in hazardous material handling, several challenges and limitations must be addressed to ensure their effective implementation. The reliability of data is crucial for the effective functioning of AI systems. Maritime environments are dynamic and unpredictable, leading to inconsistencies in data collection. Sensors may not function optimally under all conditions due to exposure to saltwater, extreme temperatures, and mechanical vibrations, which can result in gaps or inaccuracies in the collected data [49; 50; 51; 52].