# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 5 Experiments
### Route Selection by Clustering  
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [29] was applied to discover and select the best data. DBSCAN was proposed by [PERSON] and can identify clusters and outliers. A clear difference before the application of DBSCAN is found in the sample.  
The first step is to cut the 146 data (for each route) between the origin and the last actual position values (147 longitudes and latitudes approximately) of the target route to adjust the sample routes to the objective route.  
The _route selection_ helps approximate all 148 AIS data's statistical behavior to the target and discards those routes with different behavior. Some of the 149 statistical values are outliers, as shown in Figure 7.  
Figure 6: A three-dimensional perspective of three features of the routes, two of which are MAD.  
The configuration parameters of DBSCAN are related to the minimum distance found at each point, and the minimum elements of clusters are defined by 3. The method applies DBSCAN to see several groups within its routes, as shown in Figure 8.  
After applying DBSCAN, the method discards all routes that do not resemble the route to predict. For better results, it is necessary to have enough data to get an accurate approximation of its best statistical values to achieve the classification's performance according to the theorem of limit central [30]. The algorithm chooses the information that justifies the use of specific routes that share similarities with the target.  
The method's comparison is with the methodological approach for extraction of the characteristics of biological signals [31], which uses the histogram class marks to make the selection of characteristics using the behavior of the distribution of biological signals.  
Figure 8: Cluster discovered using DBSCAN. Those routes in the group (tagged as Similar) have a similarity in statistical properties and help predict and estimate the route-target.  
Figure 7: The actual and predicted routes.  
The classmark allows a representative point of each histogram interval to be obtained and then a frequency polygon to be created. A frequency polygon is built with a dynamic prototype and biological signals to get a graphic form to represent the original data.  
Table 2 shows the comparative results of route selection for each method, which are almost all similar. The differences are in the features. PreMovEst can use a small number of features to select routes. In contrast, the methodological approach for the extraction of biological signals' characteristics needs all features, multiplying the number of latitude and longitude features.  
For prediction and route estimation, the selection of all routes that resemble the current route was made. First, an Artificial Recurrent Neural Network (ARNN) with Long Short-Term Memory (LSTM) was used with historical data as continual input streams. As the second technique, multivariate imputation by chained equations used a statistical method for handling missing data.  
The sample contains almost 158,274 records for training in the case of an Artificial Neural Network (ANN). For the application of MICE, all records were used, including records of the actual vessel target.  
In addition, for both techniques, only longitude and latitude were used to save computational power.