The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea
The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operatio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/5/1093 |
| _version_ | 1797599492541775872 |
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| author | Bowen Xing Liang Zhang Zhenchong Liu Hengjiang Sheng Fujia Bi Jingxiang Xu |
| author_facet | Bowen Xing Liang Zhang Zhenchong Liu Hengjiang Sheng Fujia Bi Jingxiang Xu |
| author_sort | Bowen Xing |
| collection | DOAJ |
| description | The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.2</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management. |
| first_indexed | 2024-03-11T03:35:10Z |
| format | Article |
| id | doaj.art-2116b9b78b2a4404acf645bcd0f6b248 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T03:35:10Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-2116b9b78b2a4404acf645bcd0f6b2482023-11-18T02:01:19ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-05-01115109310.3390/jmse11051093The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China SeaBowen Xing0Liang Zhang1Zhenchong Liu2Hengjiang Sheng3Fujia Bi4Jingxiang Xu5College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaShanghai Zhongchuan NERC-SDT Co., Ltd., Shanghai 201114, ChinaDaishan County Transportation Bureau, Zhoushan 316299, ChinaBeijing Mingzhou Technology Co., Ltd., Beijing 316299, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaThe goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.2</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.https://www.mdpi.com/2077-1312/11/5/1093AISinterpolation algorithmensemble learningBayesian optimization algorithm |
| spellingShingle | Bowen Xing Liang Zhang Zhenchong Liu Hengjiang Sheng Fujia Bi Jingxiang Xu The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea Journal of Marine Science and Engineering AIS interpolation algorithm ensemble learning Bayesian optimization algorithm |
| title | The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea |
| title_full | The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea |
| title_fullStr | The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea |
| title_full_unstemmed | The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea |
| title_short | The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea |
| title_sort | study of fishing vessel behavior identification based on ais data a case study of the east china sea |
| topic | AIS interpolation algorithm ensemble learning Bayesian optimization algorithm |
| url | https://www.mdpi.com/2077-1312/11/5/1093 |
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