## 3 AI Implementations in Maritime Transport Systems
### AI in Risk Analysis and Management
AI-driven predictive analytics also play a crucial role in voyage planning. By forecasting potential risks along specific routes, AI systems can recommend safer and more efficient navigation strategies. This capability is particularly valuable in avoiding hazardous weather conditions and congested shipping lanes, thereby enhancing the safety and efficiency of maritime operations [14; 22]. However, predictive models also have their limitations, which can significantly affect the results generated by the model. The main limitation of predictive systems stems from the very nature of forecasting--there is no certainty that the predicted events will actually happen. Therefore, it is important to remember that predictive outcomes can only be probabilities, not certain facts. Additionally, many predictive models are based on certain assumptions, such as linearity or independence of variables. If these assumptions are incorrect, the model may generate inaccurate predictions. It is also important to be aware of the possibility of overfitting the model to the training data or of training a model that is too general (underfitting). Overfitting the model to the data makes it less effective in forecasting with new data. On the other hand, a model that is too general may fail to capture important patterns. The importance of data quality and availability cannot be overlooked, as predictive models are only as good as the data on which they are based. Poor data quality, missing information, or data bias can lead to incorrect results. It is therefore crucial that data is properly collected, stored, and managed so that a reliable and accurate predictive model can be created. These limitations of the predicting models may be relevant when using models to predict the weather in a given area and to determine a safe route for a ship from port to port. A model trained on historical data may fail to take into account the variables of current weather conditions or perform poorly in seasonality. This can result in the ship being directed into unsafe navigation areas or, conversely, in the model incorrectly predicting bad weather and forcing the ship to take a longer and less economical route. This in turn leads to an increase in fuel consumption, causing costs and higher emissions of environmentally harmful pollutants. These constraints also affect the predictive models used to optimize engine performance and predict failures. An AI-based predictive model that is supposed to adjust engine parameters in real time to ensure optimal engine performance and lead to fuel savings and emission reductions may in fact steer the engine inefficiently and degrade expected performance. Another important aspect is the models used to support the ship's decision to avoid collision with another vessel or land obstacles. Such models must be monitored by humans in real time, as the variability of the maritime environment and the dynamic movement of the ship are incredibly difficult to predict. Over-reliance on model predictions could lead to disasters in the maritime environment. When using predictive models in the maritime environment, especially for systems that are used in real time on board ships to support human decision-making, it is crucial to ensure proper crew training and awareness of the potential limitations of such models.