Modeling of wave-induced drift based on stepwise parameter calibration
The motion of waves in water causes the slow movement of drifting sea targets—a phenomenon usually ignored in target-drift prediction models for maritime search and rescue (SAR). This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1532757/full |
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| author | Kui Zhu Xueyao Chen Lin Mu Lin Mu Lin Mu Dingfeng Yu Dingfeng Yu Runze Yu Zhaolong Sun Tong Zhou Tong Zhou |
| author_facet | Kui Zhu Xueyao Chen Lin Mu Lin Mu Lin Mu Dingfeng Yu Dingfeng Yu Runze Yu Zhaolong Sun Tong Zhou Tong Zhou |
| author_sort | Kui Zhu |
| collection | DOAJ |
| description | The motion of waves in water causes the slow movement of drifting sea targets—a phenomenon usually ignored in target-drift prediction models for maritime search and rescue (SAR). This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently sized SAR targets—an offshore fishing vessel (OFV) and a person in the water (PIW)—using parameter stepwise calibration and machine-learning (ML) methods. The sample of wave-induced drift velocity was obtained by gradually separating current-induced (CI) drift’s and wind-induced (WI) drift’s influence from the target-drift velocity using the least-square method and AP98 model. A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. Finally, the drift trajectories considering the influence of waves were fitted and verified based on 2 independent samples respectively. Compared with the force analysis method, the accuracy of the ML methods in the verification test was higher. In addition, the results show that for OFVs, considering wave-induced drift’s influence in the ensemble-trajectory prediction could improve the simulation accuracy. However, for a PIW, no significant improvement was observed. This result also indicates that wave-induced drift may not be simply ignored in large SAR targets’ drift prediction. |
| format | Article |
| id | doaj-art-ca9006aea33e49429fd1d4ddd3fdeb28 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-ca9006aea33e49429fd1d4ddd3fdeb282025-01-14T05:10:18ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011110.3389/fmars.2024.15327571532757Modeling of wave-induced drift based on stepwise parameter calibrationKui Zhu0Xueyao Chen1Lin Mu2Lin Mu3Lin Mu4Dingfeng Yu5Dingfeng Yu6Runze Yu7Zhaolong Sun8Tong Zhou9Tong Zhou10College of Electrical Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Electrical Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Electrical Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan, ChinaHubei Key Laboratory of Marine Electromagnetic Detection and Control, Wuhan, ChinaElectrical Engineering Department, Wuhan Second Ship Design and Research Institute, Wuhan, ChinaStrategic Consulting Center, PipeChina Engineering Technology Innovation Co. Ltd., Tianjin, ChinaCollege of Electrical Engineering, Naval University of Engineering, Wuhan, ChinaHubei Key Laboratory of Marine Electromagnetic Detection and Control, Wuhan, ChinaElectrical Engineering Department, Wuhan Second Ship Design and Research Institute, Wuhan, ChinaThe motion of waves in water causes the slow movement of drifting sea targets—a phenomenon usually ignored in target-drift prediction models for maritime search and rescue (SAR). This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently sized SAR targets—an offshore fishing vessel (OFV) and a person in the water (PIW)—using parameter stepwise calibration and machine-learning (ML) methods. The sample of wave-induced drift velocity was obtained by gradually separating current-induced (CI) drift’s and wind-induced (WI) drift’s influence from the target-drift velocity using the least-square method and AP98 model. A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. Finally, the drift trajectories considering the influence of waves were fitted and verified based on 2 independent samples respectively. Compared with the force analysis method, the accuracy of the ML methods in the verification test was higher. In addition, the results show that for OFVs, considering wave-induced drift’s influence in the ensemble-trajectory prediction could improve the simulation accuracy. However, for a PIW, no significant improvement was observed. This result also indicates that wave-induced drift may not be simply ignored in large SAR targets’ drift prediction.https://www.frontiersin.org/articles/10.3389/fmars.2024.1532757/fulltarget-drift predictionsearch and rescuewave-induced driftAP98 modelensemble simulations |
| spellingShingle | Kui Zhu Xueyao Chen Lin Mu Lin Mu Lin Mu Dingfeng Yu Dingfeng Yu Runze Yu Zhaolong Sun Tong Zhou Tong Zhou Modeling of wave-induced drift based on stepwise parameter calibration Frontiers in Marine Science target-drift prediction search and rescue wave-induced drift AP98 model ensemble simulations |
| title | Modeling of wave-induced drift based on stepwise parameter calibration |
| title_full | Modeling of wave-induced drift based on stepwise parameter calibration |
| title_fullStr | Modeling of wave-induced drift based on stepwise parameter calibration |
| title_full_unstemmed | Modeling of wave-induced drift based on stepwise parameter calibration |
| title_short | Modeling of wave-induced drift based on stepwise parameter calibration |
| title_sort | modeling of wave induced drift based on stepwise parameter calibration |
| topic | target-drift prediction search and rescue wave-induced drift AP98 model ensemble simulations |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1532757/full |
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