Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

Adibi, Pedram; Pranovi, Fabio; Raffaetà, Alessandra; Russo, Elisabetta; Silvestri, Claudio; Simeoni, Marta; Soares, Amílcar; Matwin, Stan

doi:10.1007/978-3-030-38081-6_7

Cited by 14 publications

(21 citation statements)

References 20 publications

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“…The quantity of landings of each vessel was uniformly distributed along the corresponding fishing segments. Hence, for each trip, the quantity of landings associated with the fishing segment was proportional to the length of the segment itself (Adibi et al, 2020;Russo, 2020). As for the fishing effort, we summed up the landings according to the regular grid of 1 km × 1 km cell size.…”

Section: Discussionmentioning

confidence: 99%

Lockdown: How the COVID-19 Pandemic Affected the Fishing Activities in the Adriatic Sea (Central Mediterranean Sea)

et al. 2021

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The coronavirus disease 2019 (COVID-19) has brought a global socio-economic crisis to almost all sectors including the fishery. To limit the infection, governments adopted several containment measures. In Italy, Croatia, and Slovenia, a lockdown period was imposed from March to May 2020, during which many activities, including restaurants had to close or limit their business. All of this caused a strong reduction in seafood requests and consequently, a decrease in fishing activities. The aim of this study is to investigate the effects of the COVID-19 in the Northern and Central Adriatic fleet, by comparing the fishing activities in three periods (before, during, and after the lockdown) of 2019 and 2020. The use of the Automatic Identification System (AIS) data allowed us to highlight the redistribution of the fishing grounds of the trawlers, mainly located near the coasts during the 2020 lockdown period, as well as a reduction of about 50% of fishing effort. This reduction resulted higher for the Chioggia trawlers (−80%) and, in terms of fishing effort decrease, the large bottom otter trawl was the fishing segment mainly affected by the COVID-19 event. Moreover, by analysing the landings of the Chioggia fleet and the Venice lagoon fleets, it was possible to point out a strong reduction both in landings and profits ranging from −30%, for the small-scale fishery operating at sea, to −85%, for the small bottom otter trawl.

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Section: Discussionmentioning

confidence: 99%

Lockdown: How the COVID-19 Pandemic Affected the Fishing Activities in the Adriatic Sea (Central Mediterranean Sea)

et al. 2021

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“…Huang et al [26] combined machine learning and semantic behavior for pattern recognition. Adibi et al [27] predicted ship behavior, analyzed and discovered ship behavior at the semantic level, and improved maritime supervisors' understanding of water traffic. However, these semantic models lack consideration of the influence of environmental disturbance and do not fully consider the constraints of COLREGs on ship behavior.…”

Section: Knowledge-driven Behavior Modelingmentioning

confidence: 99%

Ontological Ship Behavior Modeling Based on COLREGs for Knowledge Reasoning

Zhong

Wen

Huang

et al. 2022

JMSE

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Formal expression of ship behavior is the basis for developing autonomous navigation systems, which supports the scene recognition, the intention inference, and the rule-compliant actions of the systems. The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) offers experience-based expressions of ship behavior for human beings, helping the humans recognize the scene, infer the intention, and choose rule-compliant actions. However, it is still a challenge to teach a machine to interpret the COLREGs. This paper proposed an ontological ship behavior model based on the COLREGs using knowledge graph techniques, which aims at helping the machine interpret the COLREGs rules. In this paper, the ship is seen as a temporal-spatial object and its behavior is described as the change of object elements in time spatial scales by using Resource Description Framework (RDF), function mapping, and set expression methods. To demonstrate the proposed method, the Narrow Channel article (Rule 9) from COLREGs is introduced, and the ship objects and the ship behavior expression based on Rule 9 are shown. In brief, this paper lays a theoretical foundation for further constructing the ship behavior knowledge graph from COLREGs, which is helpful for the complete machine reasoning of ship behavior knowledge in the future.

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“…In the absence of data to estimate effort directly, effort can sometimes be predicted or inferred from other characteristics of the fishery using models (e.g., McCluskey and Lewison, 2008;Greenstreet et al, 2009;Soykan et al, 2014;Johnson et al, 2017;Adibi et al, 2020), although their accuracy may be difficult to validate and may rely on unrealistic or unsupported assumptions or inaccurate information. For example, fish catch (landings) has been used as a proxy for fishing effort, either directly or through models, but landings data themselves are often inaccurate (e.g., Batista et al, 2015;Pauly and Zeller, 2016).…”

Section: Estimating Fishing Effort In Practicementioning

confidence: 99%

Estimating Bycatch Mortality for Marine Mammals: Concepts and Best Practices

et al. 2021

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Fisheries bycatch is the greatest current source of human-caused deaths of marine mammals worldwide, with severe impacts on the health and viability of many populations. Recent regulations enacted in the United States under the Fish and Fish Product Import Provisions of its Marine Mammal Protection Act require nations with fisheries exporting fish and fish products to the United States (hereafter, “export fisheries”) to have or establish marine mammal protection standards that are comparable in effectiveness to the standards for United States commercial fisheries. In many cases, this will require estimating marine mammal bycatch in those fisheries. Bycatch estimation is conceptually straightforward but can be difficult in practice, especially if resources (funding) are limiting or for fisheries consisting of many, small vessels with geographically-dispersed landing sites. This paper describes best practices for estimating bycatch mortality, which is an important ingredient of bycatch assessment and mitigation. We discuss a general bycatch estimator and how to obtain its requisite bycatch-rate and fisheries-effort data. Scientific observer programs provide the most robust bycatch estimates and consequently are discussed at length, including characteristics such as study design, data collection, statistical analysis, and common sources of estimation bias. We also discuss alternative approaches and data types, such as those based on self-reporting and electronic vessel-monitoring systems. This guide is intended to be useful to managers and scientists in countries having or establishing programs aimed at managing marine mammal bycatch, especially those conducting first-time assessments of fisheries impacts on marine mammal populations.

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Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

Cited by 14 publications

References 20 publications

Lockdown: How the COVID-19 Pandemic Affected the Fishing Activities in the Adriatic Sea (Central Mediterranean Sea)

Lockdown: How the COVID-19 Pandemic Affected the Fishing Activities in the Adriatic Sea (Central Mediterranean Sea)

Ontological Ship Behavior Modeling Based on COLREGs for Knowledge Reasoning

Estimating Bycatch Mortality for Marine Mammals: Concepts and Best Practices

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