# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**From Day**} & \multirow{2}{*}{**To Day**} & **dCost at** & **Mean dCost at** & **Mean dCost at** \\  & & **STW = 14 kn** & **STW = 12-14 kn** & **STW = 12-14 kn** \\  & & & **and Day 400** \\ \hline Laden & 51 & 347 & $2791 & $2317 & $2724 \\ \hline Ballast & 31 & 297 & $1807 & $2138 & $2714 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Estimated added fuel cost per day for the laden and ballast loading conditions, considering different values of the Days feature. report sea height and the high-frequency significant wave height data from the weather provider during their overlapping period. This analysis revealed a mean difference of \(-0.02\) m and a standard deviation of 0.84 m, suggesting that while the noon report data provides a reasonable approximation, there is still a notable degree of variability around the daily average value. To further address the limitations of using noon report wave data, a sensitivity analysis could be conducted to explore the impact on the performance of the ML model. This could involve comparing the results obtained using noon report wave heights against an alternative approach that utilizes high-frequency wave data. However, such data in the current study were not available for the entire set of periods used for the training and application of the ML models. Future research may benefit from access to higher-resolution weather data, which could provide a more accurate representation of the vessel's operating environment. Moreover, following the discussion of current speed as an input feature, the use of SOG instead of STW as an input feature is worth exploring, as SOG would inherently capture the effect of the current. In future research, we will compare model performance with and without the inclusion of the current feature to verify if similar or improved results can be achieved without its direct incorporation. Another limitation of Model-1 is the fact that the model was trained on data from only a 3-month period; thus, it has not encountered enough operational conditions to fully represent the complete profile of the vessel, especially its loading condition. Therefore, when the model is applied to operating conditions significantly different from what it was trained on, the model can produce inaccurate extrapolated predictions. This is likely the cause of the unexpected decreasing power increase trend shown in Figure 12b. On the other hand, a longer period for this approach would deviate from the assumption of the unchanged fouling status. Moreover, in the development stage of the ML models, only tree-based algorithms were evaluated, given their superior performance in the literature. The model used in this study is a quasi-static model, not a time series-based model. Therefore, more sophisticated, temporally aware algorithms, like LSTMs, were not explored in this study. The reason for using time (specifically, the Days feature) is that, in the biofouling problem, time has a long-term impact on the ship's performance and, consequently, on the power requirements. Thus, the model can capture the gradual, long-term deterioration of performance due to biofouling accumulation even though the model itself is not a dynamic, time series-based model. However, it would be beneficial to explore ANNs as well as LSTMs and compare their performance with tree-based methods, as both have demonstrated strong capabilities in similar applications (see for example [11; 18; 38]). Such an examination will be carried out in a future study. In addition, it could be interesting to explore a feature that directly reflects the number of days the ship is idle in port while at anchor or berthed. Depending on the environmental conditions in the port, biofouling can accumulate significantly during these idle periods. In the current study, the Days feature was used to capture the effects of both sailing and idle time on biofouling accumulation.