Ship navigation and fuel profiling based on noon report using neural network generative modeling

Hadi, J and Tay, Z Y and Konovessis, D (2022) Ship navigation and fuel profiling based on noon report using neural network generative modeling. Journal of Physics: Conference Series, 2311 (1). 012005. ISSN 1742-6588 (https://doi.org/10.1088/1742-6596/2311/1/012005)

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Abstract

Harbor craft historical routes contain valuable information on how the experienced crews navigate around the known waters while performing jobs. The noon report logs each job timeframe which can be used to segregate the time-series positional data as routes. Other information from the noon report such as fuel consumption could be associated with a particular job as well. This paper offers a solution to encompass crew navigational experience into neural network models. The variational autoencoder, which is a generative model, can capture the routes into a knowledge base model. The same variational autoencoder is also able to train other neural networks to make predictions of route and fuel consumption based on job metadata (I.e., job duration, activity area, and route classification). The predicted routes could be used as a cost map for pathfinding algorithms such as A* or Dijkstra.

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