The original purpose of Vessel Monitoring System(VMS) is for enforcement and control of vessel sailing. With the application of VMS in fishing vessels, more and more population dynamic studies have used VMS data to im...The original purpose of Vessel Monitoring System(VMS) is for enforcement and control of vessel sailing. With the application of VMS in fishing vessels, more and more population dynamic studies have used VMS data to improve the accuracy of fisheries stock assessment. In this paper, we simulated the trawl trajectory under different time intervals using the cubic Hermite spline(c Hs) interpolation method based on the VMS data of 8 single otter trawl vessels(totally 36000 data items) fishing in Zhoushan fishing ground from September 2012 to December 2012, and selected the appropriate time interval. We then determined vessels' activities(fishing or non-fishing) by comparing VMS speed data with the corresponding speeds from logbooks. The results showed that the error of simulated trajectory greatly increased with the increase of time intervals of VMS data when they were longer than 30 minutes. Comparing the speeds from VMS with those from the corresponding logbooks, we found that the vessels' speeds were between 2.5 kn and 5.0 kn in fishing. The c Hs interpolation method is a new choice for improving the accuracy of estimation of sailing trajectory, and the VMS can be used to determine the vessels' activities with the analysis of their trajectories and speeds. Therefore, when the fishery information is limited, VMS can be one of the important data sources for fisheries stock assessment, and more attention should be paid to its construction and application to fisheries stock assessment and management.展开更多
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 ...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.展开更多
基金supported by the National Natural Science Foundation (No. 40801225)the Natural Science Foundation of Zhejiang Province (No. LY13D 010005)Young academic leader climbing program of Zhejiang Province (grant number pd 2013222)
文摘The original purpose of Vessel Monitoring System(VMS) is for enforcement and control of vessel sailing. With the application of VMS in fishing vessels, more and more population dynamic studies have used VMS data to improve the accuracy of fisheries stock assessment. In this paper, we simulated the trawl trajectory under different time intervals using the cubic Hermite spline(c Hs) interpolation method based on the VMS data of 8 single otter trawl vessels(totally 36000 data items) fishing in Zhoushan fishing ground from September 2012 to December 2012, and selected the appropriate time interval. We then determined vessels' activities(fishing or non-fishing) by comparing VMS speed data with the corresponding speeds from logbooks. The results showed that the error of simulated trajectory greatly increased with the increase of time intervals of VMS data when they were longer than 30 minutes. Comparing the speeds from VMS with those from the corresponding logbooks, we found that the vessels' speeds were between 2.5 kn and 5.0 kn in fishing. The c Hs interpolation method is a new choice for improving the accuracy of estimation of sailing trajectory, and the VMS can be used to determine the vessels' activities with the analysis of their trajectories and speeds. Therefore, when the fishery information is limited, VMS can be one of the important data sources for fisheries stock assessment, and more attention should be paid to its construction and application to fisheries stock assessment and management.
基金Supported by the Public Welfare Technology Application Research Project of China(No.LGN21C190009)the Science and Technology Project of Zhoushan Municipality,Zhejiang Province(No.2022C41003)。
文摘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.
