RedSeaAtlas: A Visual Analytics Tool for Spatio-temporal Multivariate Data of the Red Sea

Afzal, Shehzad; Ghani, Sulaiman Ab; Tissington, Garth; Langodan, Sabique; Dasari, Hari Prasad; Raitsos, Dionysios Ε.; Gittings, John A.; Jamil, Tahira; Srinivasan, Madhusudhanan; Hoteit, Ibrahim

doi:10.2312/envirvis.20191101

Cited by 4 publications

(1 citation statement)

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“…Extracted documents revealed that data analysis/analytics techniques are increasingly being used to gather insights from the deep sea fishing challenges (European Union, 2019). For instance, multiple simulation models and observational datasets have been used in the Red Sea to monitor ocean winds, and weather forecasting (Afzal et al, 2019); Bayesian hierarchical methods are used in the analysis of transboundary fisheries (Midway et al, 2016); the DBSIRM model-a big data analytics technology is being used in the Yellow sea for fish index assessments, sustainable growth and development (Wang et al, 2022); the PesKAAS application has been used in the Timor and Banda sea to give near real-time information on fish catch and effort (Tilley et al, 2020); in the Dover straight, Alessandrini et al ( 2016) used predictive analysis, information fusion and visual analytics for automatic identification of structural abnormalities in fishes and prediction of vessel itineraries; and Mondragon et al (2021) used predictive analytics and time series analysis for ocean monitoring, food supply chain and spot trends in fisheries to inform decision making in Canada's North Atlantic and Labrador sea. Other breakthroughs in the deep sea include the assessment of potential habitat extent in deep-sea cold coral zones and submarine canyons using PCA and cluster analysis (Bargain et al, 2018).…”

Section: Relevance In Deep-sea Fisheries Managementmentioning

confidence: 99%

Relevance of Data Analytics in Sustainable Fisheries Management: An Evidence-Based Study

Lukambagire,

Matovu,

Rao R.

et al. 2023

ASP.FC

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Aim of the study: The value of fish resources to socioeconomic development is immense. However, there is growing evidence that loss of quality in the marine environments e.g. marine waters at sea and coastal freshwater zones is threatening the availability of livelihood resources e.g. fish resources evidenced by the dwindling fisheries resources that promote sustainable livelihood. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to boost marine water environment management and promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable practices e.g. marine water pollution that leads to loss of wild catches and leads to unsustainable fishing practices. Material and methods: Owing to this we use a mixed research methods approach involving a systematic literature review and field survey in Alappad to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems e.g. marine water quality loss e.g. due to pollution. Results and conclusions: Findings documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change, and marine water quality loss and implement new policies and regulations thus promoting sustainable fisheries management. Local perspectives of coastal communities could also guide the best data analytics tool or method to be used.

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