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Development of ship behavior prediction model by using Recurrent Neural Network

  • SHIO Yoshihiro
    横浜国立大学大学院 理工学府
  • ITOH Hiroko
    海上・港湾・航空技術研究所 海上技術安全研究所 海洋リスク評価系
  • KAWAMURA Yasumi
    横浜国立大学大学院 工学研究院
  • KAWASHIMA Sonoko
    海上・港湾・航空技術研究所 海上技術安全研究所 海洋リスク評価系

Bibliographic Information

Other Title
  • 再帰型ニューラルネットワークを用いた船舶の動静予測モデルの開発

Abstract

<p>It is necessary to develop a system that reduces the load on the marine traffic control because its work is manual and heavy. In this study, we created a ship behavior prediction model using Recurrent Neural Network (RNN) to explore the possibility of marine traffic control and ship maneuvering support by machine learning. Specifically, we predicted the position and course of a ship that would go through the bend of the Uraga Channel from 5 items (length, width, course, speed and position) and displayed on a map. It shows that the effectiveness of ship behavior prediction by machine learning has been confirmed.</p>

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