IUU fishing and transhipment identification with the miss of AIS data using Neural Networks

Abstract

Exploitation of maritime natural resources in Indonesia is still widespread. Efforts to monitor illegal fishing and transshipment practices are still less than optimal due to the limited ability of monitoring instruments. The loss of automatic identification system (AIS) data has an impact on weakness in the ship’s motion monitoring system. The weakness of the system in the previous research, without regard to data losses so that in real identification of illegal fishing and transshipment, it becomes less accurate and valid. Losses data it means as missing of some the data in along ship trajectory. This research designs system integration with predictor to identify the occurrence of illegal fishing and transshipment in the presence of missing AIS data. Predictor are designed using recurrent neural networks (RNN) and system integration is designed using artificial neural networks (ANN). Predictor and system integration are simulated, tested and validated using data of real ship that committed illegal fishing and transshipment. Data achieved from the marinetraffic.com and NASDEC-ITS data centers. The validation results show results from the predictor can be used as input for system integration and system integration, and it has a high accuracy.

Research on Vessel Speed Heading and Collision Detection Method Based on AIS Data

“…When dealing with related problems, GA algorithm will transform them into the biological evolution process, which will be eliminated according to the survival of the fittest principle. Finally, the evolution model will converge to the optimal individual, namely, the optimal solution of the problem [10]. e specific process is shown in Figure 3.…”

Detecting communities at high-risk of IUU fishing: networks of shadow encounters in Area 81 of the Western Central Pacific

“…In furtherance of this aim, GFW collaborated with Oceana, SkyTruth, and Google to develop an automated vessel monitoring system (AIS) detection tool that uses satellite technology to identify likely transshipments between vessels. Given the lack of available transshipment data, GFW used information about potential vessel-to-vessel interactions as a proxy measure of vessel encounters (Masroeri et al, 2021). GFW defines an encounter as a continuous interaction between a carrier and fishing vessel lasting over two hours.…”

“…The automatic detection algorithm classifies vessels as interacting when they are positioned within 500 meters of each other and are at least 10 kilometers from a coast. Despite the potential for missing data (Masroeri et al, 2021;Kumar et al, 2022;Masroeri et al, 2022), GFW AIS data is arguably one of the most complete sources of information used at local and global scales to investigate potential IUU fishing and at-sea transshipments [e.g (Mazzarella et al, 2014;Miller et al, 2018;Purivigraipong, 2018;Welch et al, 2022)]. Step 1 involved identifying all encounters between 21 carriers and 141 fishing vessels observed to interact within the FAO Area 81 during the study period.…”

“…To extend this investigation, future research could evaluate potential change mechanisms, such as preferential attachment, more directly with dynamic multivariate stochastic actor-oriented modeling to add greater statistical rigor to prediction models [e.g (Kalish, 2020)]. Also, machine learning and other strategies can be used to impute illegal harvesting and missing transshipment encounters, i.e., detect unobserved events (Masroeri et al, 2021;Kumar et al, 2022;Masroeri et al, 2022). This would allow conservation researchers to test the robustness of findings under different scenarios of data completeness.…”

Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics

“…This is due to the rapid growth of machine learning and artificial neural networks. A number of multi-feature models based on AIS trajectory points are also suggested to further increase detection accuracy [22][23][24].…”