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A fishing vessel operational behaviour identification method based on 1D CNN-LSTM

Published online by Cambridge University Press:  13 January 2025

Rongfei Xia ,
Lijie Xu ,
Yiqun Xu  and
Yifei Chen
Rongfei Xia
Affiliation:
Chengyi College, Jimei University, 199 Jimei Avenue, Xiamen 361021, China
Lijie Xu
Affiliation:
School of Marine Engineering, Jimei University, 176 Shigu Road, Xiamen 361021, China
Yiqun Xu
Affiliation:
School of Marine Engineering, Jimei University, 176 Shigu Road, Xiamen 361021, China
Yifei Chen*
Affiliation:
School of Marine Engineering, Jimei University, 176 Shigu Road, Xiamen 361021, China
*
*Corresponding author: Yifei Chen; Email: 202261000199@jmu.edu.cn
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Abstract

The identification of fishing vessel operations holds significant importance in addressing fishing industry issues, such as overfishing and illegal, unreported and unregulated fishing (IUUF). Many countries utilise data from vessel monitoring systems (VMSs) or automatic identification systems (AISs) to monitor fishing activities. These data include vessel trajectories, headings and speeds, among others. We aimed to analyse the fishing behaviours of three types of fishing gear used by vessels (trawl, purse seine and gill net) and identify the types of gear employed by the vessels. Therefore, a 1D CNN-LSTM fishing vessel operational behaviour prediction model was proposed by combining a one-dimensional convolutional (1D CNN) neural network and a long short-term memory (LSTM) neural network. The model utilises 1D CNN to extract local features from fishing vessel trajectories and employs LSTM to capture the time series information in the data, eventually classifying fishing gears. The results show that the proposed model achieves a classification accuracy of 92% in categorising fishing vessel operational trajectories. This study significantly contributes to preventing IUUF, curtailing overfishing, and enhancing fisheries management strategies.

Keywords

deep learningconvolutional neural networklong short-term memory networkfishing vessel behavioural assessment

Information

Type
Research Article
Information
The Journal of Navigation , Volume 77 , Issue 3 , May 2024 , pp. 337 - 346
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Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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