2018 3rd International Conference on Information Technology and Industrial Automation (ICITIA 2018) ISBN: 978-1-60595-607-7
Information Mining of Fishing Vessel
Based on the Data of Beidou VMS
Shengmao Zhang
ABSTRACT
Marine fishery has become one of the largest industries in the application of China's own Beidou navigation system. It has more than 5 thousand offshore fishing boats installed Beidou terminal. Fishing location, speed, heading and other information can be recorded by the terminal. These data can be used in data mining such as fishing type identification, state estimation and voyage extraction, fishing boats back and fishing effort calculation. The data mining results can be used in illegal fishing behavior recognition in prohibited fishing areas, fishery agreement area, or fishing off season. They are also can be used in fishing intensity monitoring, estimation of fishing catch and fishing vessel tracing. The mining results can provide
abundant data reference for the refinement of the fishing vessels management.1
KEYWORDS
Beidou Satellite Navigation System; Data Mining; Fishing Vessel Management; Vessel Monitoring System
INTRODUCTION
The original vessel monitoring system (VMS) was formed from the fishery monitoring progress in 1988 of Portugal. With the reinforce of the international fishery management after 90s in 20th century, the fishery activity monitoring and management have gradually became a significant part of the marine fishery resource
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protection. The international community have requested to enhance the management and maintenance of the fishery resources, and every coastal states also have gradually enhanced the fishery resource management of their sea areas. America, Australia,New Zealand and many other countries have already invented their national VMS to implement the monitoring of the fishing boats [1]. In 1996, the European Union forced all the fishing boats which length were over 24 meters to install VMS among Europe, and expanded the range to 12 meters in 2012.So far, the America, Europe, Japan and many other marine fishery oriented countries and regions have already established a sophisticated vessel monitoring system and applied it in reality.
Many developing countries and regions also have established the marine vessel position monitoring system to collect the fishing boats’ working and catches information in order to manage the marine fishery industry and fishing boats. Now ,basically almost every flag states and coastal fishery states have adopted VMS as a monitoring method to manage and maintain their fishery resources, the VMS enters into a worldwide flying development period.
The fishing boats in our coastal sea area were mainly installed the Beidou navigation system terminal which is one kind of the VMS. The Beidou navigation system integrate the navigation function and communication function into one form which allows it to get the fishing boats position data with higher time-space precision, and that made the China marine fishery became one of the biggest application industries of the Beidou navigation system. Our coastal provinces have established and applied the vessel monitoring system by means of this Beidou satellite one after another, thus the real-time vessel position monitoring system have been popularized step by step. There are more than 50 thousands coastal mobile catching boats have installed the Beidou terminal, which have provided a new technique for fishing boat’s catching behavior research [2]. In the catching process, the statuses, catching behaviour, catching habits, etc. can be recorded through the time, position, speed, orientation, rate of turn and other features. The catching behavior features combined with the regulation of the fishing boats is one of the important reference information, but the catching behavior is effected by many factors, such as the experience of the captain, catching techniques, features of the fishing boats, variation of the fishing areas, the fishery agreement area among China, Japan, Korea and Vietnam, the regulation of fishing off season from north to south in our coastal area, the coastal prohibited fishing zone line for motor trawler and so on. In this paper, information mining of fishing vessels were introduced based on the data of Beidou VMS.
BEIDOU DATA
satellite ground station; the collected data will be saved into the data base after preprocessing; the different statuses and behaviors of different types of fishing boat can be recorded through speed, orientation, rate of turn and etc. in the catching process, the spatial resolution of Beidou data is 10m and the sample interval is 3min,the catching process of the fishing boats can sustain a couple of hours in general, and from the Nyquist sampling theorem (the sample frequency is 5 to 10 times over the highest signal frequency in the practical using )we can get the wanted information.
By the end of 2014,59182 catching fishing boats,3613 fishing breeding boats,1981 fishing rafts,3165 fishing transport fishing boats,117 oil boats and 25 Engineering boats, almost 70thousands boats that have installed the Beidou terminal in total according to the statistical data of installed fishing boats from the Beidou operators[2] .There are about 180 thousands automatic catching fishing boats in our country, and among these boats there are about 50 thousands boats satisfied the condition and have installed the Beidou terminal according to China Fishery Statistical Yearbook. The provinces which have over 10 thousands terminals are Zhejiang province and Liaoning province; Shandong ,Jiangsu, Guangxi and Guangdong province have more than 3 thousands Beidou terminals, the other provinces all have terminals within 1 thousand[2].
BEIDOU POSITION DATA MINING OF FISHING VESSELS
From the Beidou fishing boat position data mining we can recognize the working type of the fishing boat, estimating the catching status ,analyzing the fishing effort, tracking fishing boats, acquiring the catching behavior features and so on, and it can provide a sufficient reference data for a elaborative fishing boat management.
THE IDENTIFICATION OF THE VESSEL WORKING TYPES
The Artificial Neural Networks (ANNs)has multi-layer network sense machine which is the most widely used and simple classifier. This classifier has been used in position data processing and classifying the working types of the fishing boats. The advantages of the ANN multi-layer network sense machine is that it does not need to know or hypothesize any probability distribution function, thus it is suitable for dealing with a complicated nonlinear datasets. The speed and course are not only being sensitive in distinguishing different working types, but also can be extracted from the Beidou data, thus it is very convenient to operate and study as well as identifying working types.
fishing gears(including the beach seine, lift net, diddle-net ,cover and other fishing gears).This paper have distinguished and analyzed three working types of the nine kinds ,namely the gillnetter, trawler and stow put by using speed and course factors of the Neural Networks models[3]. And we have distinguished the working types by using the corresponding speed and course data of the 2014 Beidou position data with 78 samples (including 15 gillnetter boats,39 trawler boats,24 stow put boats); and the result illustrated that the accuracy of the data based on the speed and based on the course are 93.6% and 91% respectively ,thus the two method both can distinguish the working type preferably and the accuracy of the method based on the speed is more preferable. Presently, the marine fishing works need application of the fishing license and the working types should be registered; but in the real working situation, some fisherman does not necessarily obey the regulations and they will mingle the fishing gears. And the illegal works will damage the marine ecological environment and fishery resources seriously. Thus from the distinguishing method based on the working speed and based the course variation we can provide assistance and evidence for the fishery resource protection and management.
JUDGEMENT OF THE VESSEL STATUS
The fishing gears were designed based on the catching targets’ ecological habits and some biological measures and the catching can be divided into active catching and passive catching based on the relative motion relation between the fishing gears with catching targets in the catching progress. The trawler is a filterable mobile fishing gear moving in the seabed or seawater and need the boat’s movement to drag the net gear, thus it can compel the fishes, shrimps, crabs and other targets passing by to enter the net, so it is an active catching method. Every haul of the trawl networking will pass three steps, namely the cast, drag and pack up. The hauling speed range from 3 to 4 knot and generally 3 to 6 times per day. The fishing boats’ speed have different status features in the hauling progress, thus we can calculate the velocity variation characteristic curve based on these features and further to calculate the threshold value from the peak value and valley value of the feature curve. Finally we can extract the status from the threshold value[4] and figure out the exactly net[5].
We can use the towed tooth type or jig type rakes, and the rake can dig the shellfish or spiny fish in the shallow seabed sediment or grit sediment, thus it was an active fishing gears similar to the trawl net, so that we can figure out the status from the velocity threshold value in the working process[6].
extract the working status from the velocity and course which have an obvious variation in these three steps[2]. The Beidou fishing boat management system have many velocity data of the boats, thus we could not only judge the working status, but also analyses the potential catching behaviors of the boats, so that to provide references for the fishing boat management.
THE EXTRACTION OF THE VOYAGE
The voyage is a common statistical magnitude which can be used to calculate economic benefit of the fishery production and investigate the distribution of the fishery resources; there are many recording method ,such as recording the voyage and survey spot manually in the investigation progress ,recording and managing the voyages by computers and etc. But these method work out only when the boats’ amount are small and largely depending on manual input and judgment, thus this manual manage method could not acquire every voyage of every boat because our country have almost 180 thousands fishing motor boats offshore. The boats’ position data recorded by Beidou terminal have the time-space characteristic thus we can extract the catching voyages based on these data[7].Firstly, making a outward buffer by using the departing port, returning port and the lands including some islands of the ports, then intersecting the buffer with the acquired boat’s position track, and making a buffer area, the generated point of intersection is the departing position or returning position, thus the track between the two points is a voyage. Finally we can acquire all the voyages by analyzing the intersecting buffer area according to the point’s tracks ordered by time sequence[7]. By researching the voyages we will gain a very good understanding of the catching situation of the boats annually and so to refine the boats’ management.
THE RETROSPET OF THE FISHING BOAT
Many different position data of one boat can form the boat’s track, and by speed and course error we can ascertain the different fishing boat’s statuses in different positions, also we can calculate the cumulative time in every status and calculate the cumulative catching time in a certain fishing ground(or fishing area)as the amount of fishing; in the meantime we can also retrospect the specific fishing grounds that the boats have been and the cumulative catching time in every fishing grounds when the boat unload the fishes in the port. The Offshore aquatic products of one port was supplied by many offshore fishing boats, thus if we can get the whole fishing boats’ position data, we can retrospect the certain source of one aquatic product and deduce the catches from the cumulative catching time. Furthermore, we can grasp the ports data which have received the catching fishes of one fishing ground(or fishing area)by analyzing the sources of the boats, also we can know the information that how many boats in the port, where are the boat came from and the cumulative catching times ,etc.[8].
Under a certain time circumstance also knowing the catching types, catching grounds and catching fish types, there is a positive correlation between the cumulative catching time with the catches; and from the cumulative catching time we can deduce the catches, retrospection the fishing grounds that the boat have been ,then knowing the fishing grounds that the aquatic product was yielded, the boats in the fishing ground at a certain time period and the sources of the boats. Thus we can evaluate aquatic products demand market influence and make an a forehand emergency deployment, hence to reduce the market fluctuations and the impacts resulting from the variation of the fishing grounds.
THE CACULATION OF THE FISHING EFFORT
The fishing effort is an important parameter of the fishery dynamic research ,namely the catching work. The traditional calculation method is to calculate the working fishing boats quantities, tonnage, horsepower, staffs, days ,technical and technological conditions and hauls, but the traditional statistical method is very time-consuming and hard to satisfy the wide-range and real time statistic needs. The fishing boat position real-time monitoring system has been gradually promoted for application in recent years, and it can get higher time precision fishing boat position data.
Using the fishing boats’ position data combined with the product of catching time with the power of the trawl fishing boats, and making the fishing effort accumulated value of the trawl fishing boats in the given area as the catching intensity of the area[9,10], hence to establish the fishing effort estimation method of our offshore fishing boats in order to give a reference to assist the fishing boats management.
ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China under Grant No. 31772899and Natural Science Foundation of Shanghai under Grant No. 06ZR14050.
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