# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 6 Results  
The purpose was to show that, after obtaining the journey knowledge base, our method helps in the selection of the routes based on the statistical behavior of each one. ANN achieved the prediction of an interval of time and the estimation of the route using the MICE Algorithm. The PreMovEst method obtained the following results: Table 3 shows a contrast between the actual data of an interval of time and the ANN's vessel movement predictions. Table 3 presents ten positions of Route 25 of Segments 15 and 16. The results show an accuracy of 80.5% in the prediction. However, if the method does not have enough information about the route to be estimated or it is too short, it loses precision. That is why we combine the two processes: prediction using neural networks and route estimation with MICE. Besides, where there is not enough information, it is necessary to obtain more historical data, which increases the processing time that depends on the amount of AIS. Figure 9: The neural network architecture. The amount of information used in the second example was more than in the first one. That allowed the accuracy to be better, at 84%. The historical AIS data samples are similar from the block to be estimated, out of 10 predictions. The absolute mean difference for latitude is 0.0015655 degrees, while the absolute mean difference for longitude is 0.00211949 degrees, as shown in Table 4.  
Concerning the MICE technique's application, as shown in Figure 10, the prediction generates the route approximation with 76.15% accuracy, using a small sample of routes. Finally, the prediction obtained an accuracy of 81.043%. However, it allows for approximating the results with the ten routes as the sample. The PreMovEst method shows that the prediction is almost the same as the real one after selecting the best routes. The best routes (series of positions) have a similarity in statistical properties after the clustering process. The routes' selection through their statistical data in the clustering allows discriminating those routes whose navigation course differs from the current route predicted. That is why they work as outliers, which is evident when clustering carried out, as they remain outside the cluster to which the route to be estimated belongs. Figure 10 compares the PreMovEst method to the Zissis method that considers a cloud infrastructure to support marine traffic. Furthermore, their system stores the trained vessel and recalls it 24 h as input for the following predictions. Our method uses data in real-time and is capable of running on a computer with GPU. Computing power is demanded, depending on the application. Our method contributes to the search for processes that allow computational savings. It is understood that a dataset can be better known if it has the largest number of characteristics. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Original Latitude** & **Predicted Latitude** & **Original Longitude** & **Predicted Longitude** \\ \hline
47.36767 & 47.88345 & \(-\)87.16083 & \(-\)86.99632 \\
47.36887 & 47.99324 & \(-\)87.16616 & \(-\)86.54012 \\
47.3715 & 48.02375 & \(-\)87.17667 & \(-\)86.56543 \\
47.37282 & 48.11034 & \(-\)87.18205 & \(-\)87.18984 \\
47.37282 & 48.11034 & \(-\)87.18205 & \(-\)87.22098 \\
47.37532 & 48.12876 & \(-\)87.19258 & \(-\)87.35465 \\
47.37667 & 48.12076 & \(-\)87.19767 & \(-\)87.65703 \\
47.378 & 48.13776 & \(-\)87.203 & \(-\)87.98583 \\
47.381.04333 & 48.22021 & \(-\)87.20817 & \(-\)87.99232 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Real data contrasted with the previously predicted path segment on the second sample.