# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 2 Related Works  
The prediction field of vessel trajectory covers different concepts: direction, speed, registered locations, and statistical analysis to identify routes. One of the most critical topics is the way to analyze data to identify patterns. [PERSON] et al. [4] proposed a framework with a progressive learning approach and without supervision to extract maritime movement patterns. Using DBSCAN removes outliers that may be location points for other routes. It is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. [PERSON] et al. [5] provided an Extended Kalman Filter (EKF) for the estimation of vessel states and used it for the prediction of vessel trajectories to integrate intelligent featuresinto Vessel Traffic Monitoring and Information Systems (VTMISs); however, the method has high computational complexity for the data selection procedures. [PERSON] et al. [6] proposed a simple prediction system using a multilayer function for a web-based system capable of real-time learning and accurately predicting any vessel's future behavior in low computational time. The selection of the historical AIS data is only related to obtaining the current data. To predict the next location in the future, it uses variables such as direction, course, latitude, longitude, and speed. [PERSON] et al. [7] proposed a system for the detection and monitoring of multiple vessels. They also showed a proposal for estimating and predicting the navigation trajectory where the historical AIS selection is based on the similarity of the routes around the positions through the clustering technique. The authors used a Bayesian vessel prediction algorithm based on a Particle Filter (PF) to enhance the vessel position prediction quality. Other works focus on predicting the next position of the vessel based on their neighborhood points and using recursively historical AIS data [8]. [PERSON] et al. [9] predicted the future position of a vessel through a genetic algorithm, and the construction of the dataset is done through the historical information of the same route to be processed. In all cases, the first step is data selection. [PERSON] et al. [10] selected the dataset based on the destination of the historical path. The most significant similarity to the travel path is predicted as the ship's destination in the forest model. The essential part of measuring two trajectories' similarity is the spatial measurement of the historical route (latitude and longitude) for practical applications. DBSCAN is used to find the spatial similarity. The principal goal to obtain better results in the prediction vessel movements, independent of prediction techniques, is the selection of the correct historical information. For example, [PERSON] considered AIS record's similarity from the point of origin to the point of arrival and used vector support machines to predict ships' movements. [PERSON] [11] used different techniques from [PERSON] et al. [12], who considered a recurring neural network based on two-way recurring gate units. In this work, we use a recurrent neural network based on long short-term memory. [PERSON] et al. [13] considered short- and long-term forecasting approaches. The problem is to estimate the arrival times of ships, and they concluded that improvements are need in the quality of the data, the volume of the data, and the mining of these distributed data, as the precision of arrival time estimates, movement predictions, and route estimates depend on these issues. It is necessary to mention that the AIS historical data quality is sometimes not adequate, as mentioned by [PERSON] et al. A process is needed to improve the quality of the datasets for use in route predictions. It highlights that the non-generalized segments in the routes are considered as trajectory outliers. These are irregular movements of the vessels when they evade barriers, collisions, and erratic traffic.