Identification of navigation characteristics of single otter trawl vessel using four machine learning models

Qi LIU; Yunxia CHEN; Haihong MIAO; Yingbin WANG

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Identification of navigation characteristics of single otter trawl vessel using four machine learning models

Aquaculture and Fisheries | Updated：2024-10-12

- Identification of navigation characteristics of single otter trawl vessel using four machine learning models
- Journal of Oceanology and Limnology Vol. 41, Issue 3, Pages: 1206-1219(2023)
- Affiliations：
  
  1. School of Fishery, Zhejiang Ocean University, Zhoushan 316022, China
  2. Zhoushan Ecological Environment Bureau Putuo Branch, Zhoushan 316100, China
  3. Marine Fisheries Bureau of Putuo District, Zhoushan 316105, China
- Author bio：
  
  yingbinwang@126.com
- Funds：
- DOI：
  CLC：
- Received：07 January 2022，
  
  Published：01 May 2023
- Accepted：
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LIU Qi,CHEN Yunxia,MIAO Haihong,et al.Identification of navigation characteristics of single otter trawl vessel using four machine learning models[J].Journal of Oceanology and Limnology,2023,41(03):1206-1219.
DOI：

LIU Qi,CHEN Yunxia,MIAO Haihong,et al.Identification of navigation characteristics of single otter trawl vessel using four machine learning models[J].Journal of Oceanology and Limnology,2023,41(03):1206-1219. DOI：

摘要

Abstract

Fishing logbook records the fishing behaviors and other information of fishing vessels. However

the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment. The fishing vessel monitoring system (VMS) can monitor and record the navigation information of fishing vessels in real time

and it may be used to improve the accuracy of identifying the state of fishing vessels. If the VMS data and fishing logbook are combined to establish their relationships

then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified. Therefore

first

a method for determining the state of VMS data points using fishing log data was proposed. Secondly

the relationship between VMS data and the different states of fishing vessels was further explored. Thirdly

the state of the fishing vessel was predicted using VMS data by building machine learning models. The speed

heading

longitude

latitude

and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013

and four machine learning models were established

i.e.

Random Forest (RF)

Adaptive Boosting (AdaBoost)

K-Nearest Neighbor (KNN)

and Gradient Boosting Decision Tree (GBDT) to predict the behavior of fishing vessels. The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve. Results show that the importance rankings of spatial (longitude and latitude) and time features were higher than those of speed and heading. The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models. RF model showed the highest prediction performance for fishing state. Meanwhile

AdaBoost model exhibited the highest prediction performance for non-fishing state. This study offered a technical basis for judging the navigation characteristics of fishing vessels

which improved the algorithm for judging the behavior of fishing vessels based on VMS data

enhanced the prediction accuracy

and upgraded the fishery management being more scientific and efficient.

关键词

Keywords

references

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