AI-Driven Fishing Ground Prediction and Route Optimization for Sustainable Fisheries in the Norwegian and Barents Seas – FISH-AI


FISH-AI aims to support a more data-driven approach to managing ocean fisheries by using large-scale maritime data and modern AI methodology to address key challenges in Norway’s fishing industry. By turning complex ocean and vessel data into actionable insights, the project aims to support safer, more sustainable, and more economically efficient fishing operations. In doing so, FISH-AI seeks to contribute to a future in which fisheries management is both environmentally responsible and operationally resilient.

Background

Norway’s fishing industry is facing growing environmental, economic, and safety-related challenges. As the country’s second-largest industry after petroleum, fisheries remain essential to coastal communities, employment, and national value creation. The sector directly employs more than 10,000 people across over 1,000 vessels and plays a central role in Norway’s maritime economy.

At the same time, the industry is under increasing pressure:

  • Environmental Challenges: Climate change is altering marine ecosystems and fish distributions, while stricter environmental requirements demand reductions in emissions and bycatch alongside increasingly complex management measures.

  • Economic Pressures: Rising operational costs and the transition toward more sustainable practices are creating significant economic strain for fishers and fishing companies.

  • Safety Risks: Fishing in the Norwegian and Barents Seas involves severe and unpredictable weather conditions in remote environments where rescue operations may take several days, making operational decisions at sea particularly high-risk.

FISH-AI aims to address these challenges by using large-scale fisheries and environmental data together with modern artificial intelligence methods to support safer, more sustainable, and more efficient fishing operations.

Objectives

The project is built around three core objectives:

  • Predict Fishing Grounds: Develop high-resolution, species-specific forecasts of fishing grounds, extending prediction horizons from 2–3 days to 7–14 days. This will support a transition from experience-based decision-making toward data-driven precision forecasting.

  • Optimize Fishing Routes: Develop multi-objective routing algorithms that balance fuel efficiency, travel time, expected catch, and safety risks. These tools will help fishers make more informed operational decisions at sea.

  • Deploy a User-Friendly Platform: Create an intuitive user platform that delivers real-time decision support and forecasting tools, ensuring practical usability and broad accessibility across the fishing industry.

Theoretical Approaches

  • Multiuple data modalities: The project will combine data from Electronic Reporting Systems (ERS), Vessel Monitoring Systems (VMS), Automatic Identification Systems (AIS), as well as oceanographic, geospatial, and acoustic datasets.

  • Deep Learning Framework: A deep learning architecture will be developed to capture both short-term dynamics and long-term patterns, combining historical behavior with real-time environmental conditions such as weather and ocean state.

  • Validation and Deployment: Models will be rigorously validated through staged testing, ranging from small-scale experiments to large-scale at-sea trials. Once validated, the algorithms will be deployed within an industry-partnered digital platform for operational use.

Ambition and Innovation

FISH-AI aims to support a more profitable, safer, and more sustainable fishing industry by improving operational efficiency, reducing costs, and lowering risk through data-driven decision support. It also strengthens sustainability by supporting more responsible use of marine resources.

The project’s innovation lies in developing algorithms that can be embedded in a practical digital platform, enabling fishers to identify fishing opportunities and plan efficient routes with higher precision than is currently possible.

The expected impacts span multiple levels. For the fishing industry, vessel owners, skippers, and fishers benefit from improved planning, efficiency, reduced costs, and enhanced safety. For policy-makers, the project provides better decision-support tools for sustainable fisheries management. More broadly, the methods are scalable to other fisheries and regions and can support education and further research.

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