The objective of this work is to show the benefits of a new eco-systemic fishing gear installed in three bottom trawlers after one year using it. The study has been based on fuel consumption reduction for the three ve...The objective of this work is to show the benefits of a new eco-systemic fishing gear installed in three bottom trawlers after one year using it. The study has been based on fuel consumption reduction for the three vessels and the catch in two of them. The new system minimizes the impact of the fishing gear on the seabed, with a reduction on the tow resistance. This generates significant fuel savings that improves the economical result of the fishery, helping the ship owners in the return of investment first, then in the future viability of the operation. Apart of the evident savings due to the fuel consumption reduction, in the long term, the ship owner will notice also savings on maintenance, both because the winches are towing with less tension （longer life for warps, brakes and hydraulic system） and the engine is running at low rpm＇s （longer life of the engine and between breakdowns）. The new fishing gear does not require any modification on the way that the fisherman is working, only replacing and/or modifying some parts or components of the fishing gear. The implementation is easy and the adjustments required could be done in a couple of days.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
文摘The objective of this work is to show the benefits of a new eco-systemic fishing gear installed in three bottom trawlers after one year using it. The study has been based on fuel consumption reduction for the three vessels and the catch in two of them. The new system minimizes the impact of the fishing gear on the seabed, with a reduction on the tow resistance. This generates significant fuel savings that improves the economical result of the fishery, helping the ship owners in the return of investment first, then in the future viability of the operation. Apart of the evident savings due to the fuel consumption reduction, in the long term, the ship owner will notice also savings on maintenance, both because the winches are towing with less tension （longer life for warps, brakes and hydraulic system） and the engine is running at low rpm＇s （longer life of the engine and between breakdowns）. The new fishing gear does not require any modification on the way that the fisherman is working, only replacing and/or modifying some parts or components of the fishing gear. The implementation is easy and the adjustments required could be done in a couple of days.
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.
