A Novel and Efficient LR-LSTM AIS Route Data Prediction for Longer Range.

Abstract

 
Abstract
The growth of technology has enabled different industries to generate an excessive amount of data- one such industry being the maritime sector. Sophisticated sensory systems installed on various vessels generate information at a very large scale which can further be used in optimizing operational efficiency, improving safety standards, and aiding in the decision-making process. Researchers have henceforth identified statistical learning methods and machine learning techniques as potent tools for excavating useful insights from this copious amount of data available. This research evaluates how these algorithms work by focusing exclusively on the analysis of sensory data collected from vessels within the maritime domain. A comparison study has been conducted between statistical learning methods (which includes regression analysis, and time series analysis) vis-a-vis machine learning approaches. The major objective of this study was to determine the most effective method for detecting anomalies while simplifying marine operations and optimizing vessel behavior. The scope of the conducted analysis is restricted to the prediction of the next trajectory points. Accurate prediction of vessel positions plays a crucial role in maritime operations, enabling efficient route planning, collision avoidance, and maritime traffic management. In this article, the authors propose a combination model that combines the benefits of Linear Regression (LR) and Long Short-Term Memory (LSTM) techniques to anticipate vessel positions based on Automatic Identification System (AIS) data. The proposed model takes advantage of the interpretability of LR and the temporal dependencies collected by LSTM to capture temporal dependencies, which improve prediction accuracy and reveal the underlying links between vessel features and future positions.
Subjects
STATISTICAL learning; TIME series analysis; MACHINE learning; REGRESSION analysis; AUTOMATIC identification

Description