Applications of machine learning and data science to the blue economy sustainable fishing and weather routing
The Blue Economy encompasses an interdisciplinary field of study aimed at achieving sustainable utilization of ocean resources while preserving the environment’s health. The importance of this concept lies in its role in achieving the Sustainable Development Goals defined by the United Nations. Nevertheless, the pursuit of economic development can often conflict with the principles of sustainability, underscoring the necessity of leveraging adequate tools to address these challenges. Data science, and particularly Machine Learning, has become a valuable tool for addressing the challenges of the Blue Economy. For example, in the field of sustainable fishing, monitoring fish populations is highly relevant and can be achieved through Machine Learning models. In another area, such as maritime transport, the implementation of weather routing tools can optimize sea routes, improving fuel efficiency and ensuring a reduction in greenhouse gas emissions. This thesis will delve into the study of sustainable fishing and weather routing in the context of the Blue Economy, applying data science techniques to improve efficiency and sustainability in both fields
Data Science, Machine Learning, Blue Economy, Sustainable fishing, Echo-sounder buoys, Weather routing, Ciencia de Datos, Economía Azul, Pesca sostenible, Boyas con ecosonda
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