# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 6 Discussion  
In this study, the models developed are data-driven and based on machine learning (ML) algorithms. This approach was chosen due to the large volume of available operational data from various sources and the improved accuracy that ML algorithms can provide in analyzing such data. Additionally, with Model-2, a quasi-static approach was followed to capture the long-term impact of biofouling on the ship's performance by incorporating time as an input feature. Specifically, the two-and-a-half-year period of available operational data with three maintenance events in between provided an opportunity to examine various hull and propeller conditions of varying performance and decide the most appropriate periods of vessel operation to perform data analysis and train and implement ML methods. It is emphasized that knowledge of maintenance events is significant when selecting the time periods to be used for training ML models as it prevents including data that entail physical processes not related to the targeted one. For example, the inclusion of the time period during which the propeller was damaged would disturb the analysis of the effect of fouling development as it would include data points corresponding to different propulsion characteristics. Moreover, feature engineering and data preparation are important steps in data-driven studies. Domain knowledge and understanding of the physics of the problem is necessary for insightful usage of parameters by combining or creating new ones. Furthermore, extensive filtering was applied to keep only the operational conditions related to open-sea navigation and within a range of key parameters that will be meaningful to the problem data in the ML models. However, considering that this a static approach for threshold definition, methods related to adaptive thresholding could be explored in future work to potentially enhance the robustness of this procedure. Data smoothing was also carried out using a simple moving average to reduce the noise of the signals. The aim was to reduce the sharp fluctuations that do not add significant information to the physical process of the problem. However, at the same time, we need to stay close to the original data to be able to capture environmental loading and other feature variations that can reveal important patterns in the data. The proper time window for smoothing in this analysis was selected based on domain knowledge; however, the addition of a statistically quantified metric to support the justification is planned in a future study. Even though a significant percentage of the initial dataset was removed from the final dataset, this is a necessary step to derive a quality dataset. Having prepared the dataset, the approach used to evaluate the biofouling condition of the hull and propeller presents a novel methodology that considers the temporal aspect and incorporates separate loading conditions in the generation of synthetic datasets. However, the study has certain limitations. Firstly, due to the unavailability of high-frequency measurements for draft and weather data during specific time periods, the corresponding noon report data had to be used, resulting in lower accuracy and increased uncertainty of the actual values. While minimal variation in the draft over a day is plausible, the wave conditions can change rapidly due to factors such as shifting wind patterns and swell conditions. Using a single daily wave height value from the noon reports does not fully capture this temporal variability of the wave conditions experienced by the vessel over a day. To quantify the potential impact of this limitation, a comparative analysis was conducted between the noon