# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 5 Experiments
### Results
#### 5.3.3 Results for Each Case  
We describe the performance measurement results of methods beyond the evolutionary computation mentioned in Section 3. Table 5 shows the main parameter values for each method. Our prior work [8] measured the error per iteration. The deep leaning method using PyTorch (e.g., DNN, RNN, and LSTM) is conceptually similar; MAE and loss were measured per epoch. The result based on the WEKA library could not measure the error according to the iteration since it could not set the iteration number internally. Table 6 shows the evaluation of measured values by building models from ML and evolutionary computation methods. Only the Seosan data was used for training. GP, MLP, RBFN, and SVR based on WEKA showed outstanding performance. However, the deep learning methods did not perform very well as the variation in data volume by case was large for the Seosan data. The difference between the numbers of Cases 1 and 2 was nine times, as shown in Table 1. The data length also has a significant impact on training because the previous event influences the next event in RNN. Table 7 shows the CPU time spent building models for each method. Evolutionary computation methods took more time than GP, MLP, and RFBN using WEKA software. Table 8 shows the experiment results with the Jeju data. Unlike the result of the Seosan data, the deep learning methods showed excellent performance. LSTM performed better than the basic RNN models, and sometimes DNN performed better. In Cases 1, 2, and 3, MLP and RBFN using WEKA software indicated good performance, whereas the evolutionary computation methods are neither good nor bad performance. The results of the RNN methods could be improved since the variation of the number of data by case is small for the Jeju data. Table 9 shows the CPU time for calculation. The computation time for the Jeju data was not much different from that of the Seosan data. Figure 6: MAE and loss values of RNN. Figure 7: MAE and loss values of LSTM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Evaluation**} & \multicolumn{4}{c}{**Case**} \\ \cline{3-6}  & & **1** & **5** & **6** & **7** \\ \hline DE & MAE & 0.0743 & 0.0286 & 0.0906 & 0.0417 \\  & NCLS & 0.9308 & 0.7377 & 0.7769 & 0.9172 \\  & MAE & 0.1238 & 0.0318 & 0.1050 & 0.0758 \\ PSO & NCLS & 0.8724 & 0.7183 & 0.7557 & 0.8375 \\ CMA-ES & MAE & 0.0743 & 0.0286 & 0.0909 & 0.0418 \\  & NCLS & 0.9308 & 0.7378 & 0.7763 & 0.9170 \\ GP & MAE & 0.0945 & 0.0290 & 0.0974 & 0.0360 \\  & NCLS & 0.9113 & 0.7431 & 0.7583 & 0.9282 \\ MLP & MAE & 0.0957 & 0.0285 & 0.1012 & 0.0320 \\  & NCLS & 0.9030 & 0.7541 & 0.7474 & 0.9353 \\  & MAE & 0.1064 & 0.0278 & 0.0986 & 0.0297 \\ RBFN & NCLS & 0.8992 & 0.7626 & 0.7508 & 0.9399 \\  & MAE & 0.0700 & 0.0301 & 0.0939 & 0.0394 \\ SVR & NCLS & 0.9355 & 0.7402 & 0.7711 & 0.9204 \\  & MAE & 0.1557 & 0.0192 & 0.1305 & 0.0571 \\ DNN & NCLS & 0.8493 & 0.8383 & 0.6879 & 0.8848 \\  & MAE & 0.1369 & 0.0270 & 0.1335 & 0.0494 \\ RNN & NCLS & 0.8555 & 0.7699 & 0.6809 & 0.8910 \\  & MAE & 0.0744 & 0.0295 & 0.1153 & 0.0301 \\ LSTM & NCLS & 0.9201 & 0.7413 & 0.7075 & 0.9408 \\  & MAE & 0.1352 & 0.1238 & 0.0656 & 0.0434 \\ MOHID & NCLS & 0.8633 & 0.7134 & 0.8480 & 0.9229 \\ \hline \hline \end{tabular} The lower the MAE values, the better. The higher the NCLS values, the better.  
\end{table} 
Table 6: Results for Seosan data.