# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 4 Conclusions  
This study proposed a data-driven methodology for the prediction of the SOG of a ship while sailing using the AIS data and noon-report marine weather data. The main findings of this study are as follows: The developed models can accurately estimate the SOG of the ships sailing under different weather conditions, load conditions, draughts, and sailing distance/direction; the results also showed that linear regression and the polynomial model gives inaccurate prediction results for SOG because \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **LR** & **Poly** & **GBR** & **XGBR** & **DTR** & **RFR** & **ETR** \\ \hline Mean [\%] & 23.55 & 40.76 & 65.59 & 97.459 & 97.209 & 98.37 & 98.45 \\ Std. [\%] & 0.102 & 0.202 & 0.2357 & 0.056 & 0.0866 & 0.055 & 0.057 \\ Min [\%] & 23.38 & 40.45 & 65.07 & 97.37 & 97.10 & 98.26 & 98.36 \\ Median [\%] & 23.55 & 40.72 & 65.70 & 97.46 & 97.19 & 98.38 & 98.44 \\ Max [\%] & 23.70 & 40.99 & 65.78 & 97.53 & 97.37 & 98.45 & 98.54 \\ Computational time [sec] & 8 & 850 & 6880 & 1514 & 312 & 2804 & 1590 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Descriptive statistics of model accuracy in 10-fold cross-validation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R2** & **RMSE** & **NRMSE** & **Computational time [sec]** \\ \hline GBR & 0.6858 & 1.0608 & 0.0617 & 908 \\ XGBR & 0.9698 & 0.3287 & 0.0191 & 257 \\ DTR & 0.9646 & 0.3559 & 0.0207 & 52 \\ RFR & 0.9831 & 0.2464 & 0.0143 & 489 \\ ETR & 0.9847 & 0.2340 & 0.0136 & 253 \\ LR & 0.2379 & 1.6522 & 0.0961 & 1 \\
3 rd order Polynomial & 0.4008 & 1.4778 & 0.0859 & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model performance for testing dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vessel Name** & **Vessel Type** & **Route** & **R2** & **RMSE** & **NRMSE** & **Data Size** \\ \hline A & Tanker & Chiba, JP to Townsville, AUS & 0.9845 & 0.0609 & 0.0077 & 2644 \\ B & Tanker & Burnie, AUS to Yokkaichi, JP & 0.9734 & 0.1506 & 0.0206 & 3351 \\ C & Cargo & Shibushi to Vancouver, CAN & 0.9827 & 0.1178 & 0.0127 & 4481 \\ D & Cargo & Marsden PT, to Singapore & 0.9881 & 0.0543 & 0.0106 & 3004 \\ E & Cargo & Westshore CAN, to & 0.9821 & 0.1038 & 0.0127 & 3014 \\  & & Gwangyang, S. XOR & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Extra trees regressor (ETR) models performance for a single route of different vessels. of the highly nonlinear tendency of SOG with time; using noon-report weather data and AIS data, various ensemble models achieved model accuracies of more than 96% as given by the \(R^{2}\) value even considering the random effects of SOG; applying hyperparameter optimization may also increase and stabilize the accuracy of a model; and ETR, which is one of the bagging ensemble models, yielded high accuracy with low computational time to predict the SOG. The suggested methodology was used for the real data with different ship types and routes, proving that this methodology can be applied to essentially for any types of vessel. In addition, while the findings of this study are expected to be used for route optimization purposes, this methodology can also be used to create models that will help the performance degradation of track vessels, and the optimization of shipping operations. Conceptualization, M.A., Y.N. and Y.S. ; data acquisition, Y.S., S.L., and Y.N. ; methodology, M.A. ; coding, M.A. and Y.S. ; validation, M.A. ; formal analysis, M.A. ; investigation, M.A. ; resources, Y.N. and I.L. ; writing-original draft preparation, M.A. ; writing-review and editing, Y.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) through GCRC-SOP (No. 2011-0030013), Korea government (MSIT) (No.