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Effective fisheries management is the key to achieve sustainable fisheries globally, while accurate monitoring of fishing vessels is essential to improve the effectiveness of management measures. Self-reported information on vessel types is often limited and may not cover all operating fishing vessels, causing incomplete monitoring in fisheries management. Therefore, a novel way to objectively identify the types of a large quantity of fishing vessels is needed. In this study, we presented an innovative integrated deep learning model by using automatic identification system (AIS) data to classify five types of fishing vessels, including gillnetter, hook and liner, trawler, fish carrier, and stow net vessel, further improving the performance of fishing vessel classification. First, we preprocessed data by removing erroneous information, dividing the vessel trajectories by day to obtain a complete and reliable dataset. Then, a multidimensional feature vector was constructed by combining the geometric, static and dynamic characteristics of fishing vessels to explain the behavioral differences of various types of fishing vessels more effectively. Finally, the feature vector was fed into an ensemble model of a two-dimensional bidirectional long short-term memory network and a convolutional neural network with an attention mechanism for training, and the prediction results were obtained through a fully connected layer. The accuracy of the ensemble model was 91.90%, which was higher than other single classifiers. The experimental results demonstrated that this method obtained remarkable performance and could be adopted to improve the precision of fishing vessel classification based on AIS data.

Monitoring and understanding the behavior of fishing vessels are important in facilitating effective management, preventing illegal fishing, informing fishing grounds and evaluating effects of harvests on fishery resources. In recent decades, a large quantity of real-time data of fishing vessels have become available with the development of vessel-tracking systems, making it possible to study the behavior of fishing vessels in high spatial and temporal resolutions. To effectively and efficiently deal with the large amount of data, algorithms from artificial intelligence (AI) are increasingly applied in the study of fishing vessel behavior. In this paper, we first introduce the various data sources for studying fishing vessel behavior and compare their pros and cons. Secondly, we review the AI methods that have been used to monitor and extract the behavior of fishing vessels from big data. Then, studies on the physical, ecological and social mechanisms affecting the behavior of fishing vessels were synthesized. Lastly, we review the applications of fishing vessel behavior in fishery science and management.

Routine maintenance of fishing vessels and wastewater discharges are primary sources of heavy metals in fishing ports. Sediment pollution assessment is necessary in fishing port management, including sediment dredging and disposal, sewage treatment facility construction, and pollution source control. In this study, sediment heavy metal contents in Qianzhen Fishing Port, the largest pelagic fishery port in Taiwan, were investigated to assess the contamination levels and related potential ecological risks using multiple sediment pollution indices. Normalization methods were applied to identify the potential sources of heavy metals in fishing port sediments. Results showed that Cu, Zn, Pb, and Cr contents in the sediments of the inner fishing port (averages of 276, 742, 113, and 221 mg/kg, respectively) were 3–5 times greater compared to those along the port entrance and outside, indicating the strong impacts of anthropogenic pollution (EFCu: 5.6–12.5; EFZn: 2.8–4.3; EFPb: 2.4–5.4; EFCr: 1.1–3.2). Copper pollution was more severe, with high maxima contamination factor (CFCu: 15.1–24.8), probably contributed by copper-based antifouling paints used in fishing vessels. The sediments in the inner fishing port are categorized as having considerable ecological risk and toxicity (mERMq: 0.61–0.91; ΣTU: 7.5–11.7) that can potentially cause adverse effects on benthic organisms. Qianzhen Fishing Port sediments can be characterized as high Cu/Fe and Pb/Fe, moderate Zn/Fe, and high total grease content, indicating that the potential sources of heavy metals are primarily antifouling paints and oil spills from the fishing vessels. This study provides valuable data for pollution control, remediation, and environmental management of fishing ports.

The South China Sea (SCS) is an important region of fishery resources. However, its fishery resources have been threatened, mainly because of overfishing. In this study, we explored the distribution of night-time fishing boats and analyzed the relationship between fishing activities and marine environmental factors in the northwestern SCS (NWSCS). Firstly, the spatiotemporal variations in nighttime fishing boats in each month of 2021 in the NWSCS were studied. Meanwhile, a fishery activity center index was used to analyze the overall fishery activity trend in the NWSCS. Finally, the spatiotemporal distribution patterns of corresponding environmental factors (i.e., Chl-a, SSS, SST, latitude, longitude) were analyzed, and the nonlinear relationship between environmental factors and fishery activities was quantitatively studied using the generalized additive model. The results showed that fishery activities were mainly distributed in the waters of Beibu Gulf and the southwest of Hainan Island. Meanwhile, there were obvious seasonal differences (i.e., trimodal distribution) in the intensity of fishery activities in the NWSCS. Chl-a was the most important impact factor with a contribution of 21.7%, followed by SSS, longitude, SST, and latitude, with contributions of 12.8%, 9.4%, 4.2%, and 0.5%, respectively. Fishery activities in the NWSCS were mainly distributed in the area with Chl-a of 0~0.35 mg/m3, SST of 21.2~26.4 °C, and SSS of 32.9~33.8 Practical Salinity Unit. This study reveals that more efforts are required to prevent IUU fishing activities for the sustainable development of marine ecosystems in the NWSCS. It is also necessary to improve remote sensing technology to support making sustainable fishing plans.

The identification of fishing vessel operations holds significant importance in addressing fishing industry issues, such as overfishing and illegal, unreported and unregulated fishing (IUUF). Many countries utilise data from vessel monitoring systems (VMSs) or automatic identification systems (AISs) to monitor fishing activities. These data include vessel trajectories, headings and speeds, among others. We aimed to analyse the fishing behaviours of three types of fishing gear used by vessels (trawl, purse seine and gill net) and identify the types of gear employed by the vessels. Therefore, a 1D CNN-LSTM fishing vessel operational behaviour prediction model was proposed by combining a one-dimensional convolutional (1D CNN) neural network and a long short-term memory (LSTM) neural network. The model utilises 1D CNN to extract local features from fishing vessel trajectories and employs LSTM to capture the time series information in the data, eventually classifying fishing gears. The results show that the proposed model achieves a classification accuracy of 92% in categorising fishing vessel operational trajectories. This study significantly contributes to preventing IUUF, curtailing overfishing, and enhancing fisheries management strategies.

The Marine Stewardship Council estimates that approximately 38 million people worldwide work in fisheries, and more than one-third of the global population is dependent on aquatic products for protein, highlighting the importance of sustainable fisheries. The FISH Safety Foundation reports that 300 fishers die every day. To achieve sustainable fisheries as a primary industry, the safety of human resources is of the utmost importance. The International Maritime Organization (IMO) and the International Labor Organization (ILO) have made efforts towards this goal, including the issuance of agreements and guidelines to reduce industrial accidents among fishing vessel workers. The criterion for applying these guidelines is usually a total ship length ≥12 m or ≥24 m. However, a vast majority of registered fishing vessels are <12 m long, and the fishers of these vessels suffer substantially more industrial accidents. Thus, we conducted a quantitative analysis of 1093 industrial accidents affecting fishers on fishing vessels <12 m in length, analyzed risk using a Bayesian network analysis (a method proposed by the Formal Safety Assessment of the IMO), and administered a questionnaire survey to a panel of experts in order to ascertain the risk for different types of industrial accidents and propose specific measures to reduce this risk.

This research addresses the critical issue of major fishing vessel accidents, which traditionally suffer from a lack of focused analysis due to their rarity and the subjective nature of their classification. We propose an innovative methodology of Peaks Over Threshold to overcome subjectivity in accident classification. This approach ensures a more representative and accurate analysis of major accidents, distinguishing them from more common, less severe incidents. Employing a Bayesian network model, we further explore the most influential factors contributing to these major accidents. The key innovation lies in our novel approach to data handling and analysis, enabling us to uncover hidden patterns and causal relationships that traditional methods often overlook. The results show that the approach proposed in this study can effectively capture the key factors of major fishing vessel accidents. This study identifies accident type, vessel-related factors, and accident location as the key influential factors leading to major accidents. The findings from our research are intended to inform sustainable fisheries management practices, promoting interventions that aim to decrease the occurrence and impact of severe maritime accidents while balancing economic, safety, and sustainable development considerations.

This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.

Open-sea fishing vessel accidents are difficult to assess systematically because no state holds exclusive jurisdiction, and reporting and investigative duties are not applied consistently. This study analyzed 67 officially reported accidents from the International Maritime Organization (IMO) Global Integrated Shipping Information System (GISIS) using a bow-tie framework combining fault tree analysis (FTA), Firth logistic regression, event tree analysis (ETA), and quantitative risk assessment (QRA). COLREG violations and watchkeeping failures dominated collisions; overload and stability issues caused capsizes; pump capacity, hull leakage, and vessel aging (≥30 years) caused sinkings. Firth regression confirmed older vessels and high beam-to-length ratios (≥0.30) significantly increased sinking likelihood. ETA and QRA estimated probabilities of 0.522 for collisions, 0.090 for capsizes, and 0.388 for sinkings, with risks of R = 0.155, 0.048, and 0.036. Because open-sea accident data rely on limited and voluntary reporting, results are preliminary. However, the bow-tie framework effectively identifies dominant causal factors and high-severity event pathways in open-sea fishing operations.