# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 5 Experiments
### Results
#### 5.3.4 Weighted Average Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multirow{3}{*}{**Evaluation**} & \multicolumn{3}{c}{**Seosan**} & \multicolumn{3}{c}{**Jeju**} & \multicolumn{3}{c}{**Integration**} \\ \cline{3-10}  & & **Weighted** & **Standard** & **Weighted** & **Standard** & **Weighted** & **Standard** & **Standard** \\  & & **Average** & **Deviation** & **Average** & **Deviation** & **Average** & **Deviation** & **Rank** \\ \hline \multirow{3}{*}{DE} & MAE & 0.0660 & 0.0001 & 0.0528 & 0.0000 & 0.0567 & 0.0001 & 3 \\  & NCLS & 0.8634 & 0.0001 & 0.8712 & 0.0001 & 0.8717 & 0.0002 & 5 \\  & MAE & 0.0979 & 0.0293 & 0.0642 & 0.0068 & 0.0569 & 0.0010 & 4 \\  & NCLS & 0.8069 & 0.0386 & 0.8418 & 0.0234 & 0.8717 & 0.0024 & 6 \\  & MAE & 0.0660 & 0.0002 & 0.0529 & 0.0001 & 0.0567 & 0.0000 & 2 \\  & NCLS & 0.8635 & 0.0004 & 0.8709 & 0.0004 & 0.8718 & 0.0000 & 4 \\  & MAE & 0.0743 & 0.0000 & 0.0554 & 0.0000 & 0.0589 & 0.0000 & 8 \\  & NCLS & 0.8541 & 0.0000 & 0.8682 & 0.0000 & 0.8675 & 0.0000 & 8 \\  & MAE & 0.0747 & 0.0033 & 0.0530 & 0.0013 & 0.0584 & 0.0007 & 7 \\  & NCLS & 0.8515 & 0.0042 & 0.8741 & 0.0036 & 0.8686 & 0.0010 & 7 \\  & MAE & 0.0793 & 0.0062 & 0.0435 & 0.0012 & 0.0556 & 0.0018 & 1 \\  & NCLS & 0.8493 & 0.0062 & 0.8989 & 0.0027 & 0.8753 & 0.0037 & 2 \\  & MAE & 0.0662 & 0.0000 & 0.0527 & 0.0000 & 0.0576 & 0.0000 & 5 \\  & NCLS & 0.8613 & 0.0000 & 0.8724 & 0.0000 & 0.8731 & 0.0000 & 3 \\  & MAE & 0.1100 & 0.0175 & 0.0465 & 0.0057 & 0.0733 & 0.0121 & 9 \\  & NCLS & 0.7986 & 0.0250 & 0.8915 & 0.0141 & 0.8487 & 0.0192 & 9 \\  & MAE & 0.0993 & 0.0223 & 0.0416 & 0.0052 & 0.0958 & 0.0326 & 10 \\  & NCLS & 0.8083 & 0.0259 & 0.9008 & 0.0143 & 0.8065 & 0.0516 & 10 \\  & MAE & 0.0702 & 0.0007 & 0.0411 & 0.0004 & 0.0579 & 0.0035 & 6 \\  & NCLS & 0.8506 & 0.0017 & 0.9040 & 0.0010 & 0.8762 & 0.0059 & 1 \\  & MAE & 0.0932 & – & 0.0789 & – & 0.0861 & – & – \\  & NCLS & 0.8201 & – & 0.8228 & – & 0.8215 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of weighted average and standard deviation values. performance for Jeju. Finally, Figure 10 presents the trajectory of a drifter predicted by RBFN with the best performance for MAE. Both ML and evolutionary search optimize the parameters. There is a slight difference in accuracy, but all of them predict similar paths. Except for the RNN series (RNN and LSTM), only the data at that point were used to predict the trajectory of a drifter at a point in hourly time. However, there is a difference in that RNN uses hidden layer neurons, which were used for prediction in previous instances, and LSTM showed the best performance as a measure of NCLS. It is believed that this is because the RNN series take into account the inertia of the drifter. Figure 8: Comparison of trajectory predicted by our CMA-ES model, trajectory predicted by an existing numerical model (MOHID), and observed trajectory for four major drifters. Figure 10: Comparison of trajectory predicted by our RBFN model, trajectory predicted by an existing numerical model (MOHID), and observed trajectory for four major drifters. Figure 9: Comparison of trajectory predicted by our LSTM model, trajectory predicted by an existing numerical model (MOHID), and observed trajectory for four major drifters.