Description:

The Fisheries Stock Prediction System is a data-driven web platform designed to help fisheries, marine biologists, and policymakers estimate the future availability of fish species in oceans, rivers, and lakes.
It uses historical catch data, water quality indicators, satellite imagery, and weather patterns to predict fish population trends and identify sustainable harvesting limits.

This system aims to prevent overfishing, support sustainable seafood production, and ensure long-term marine biodiversity.

Key Features:

1. Historical Stock Data Analysis – Displays past trends in fish population and catch volumes for different species.

2. Water Quality & Habitat Monitoring – Integrates IoT sensors and satellite data to track temperature, salinity, oxygen levels, and pollution.

3. Seasonal Pattern Detection – Identifies breeding and migration seasons for targeted fishing strategies.

4. Predictive Analytics – Uses machine learning models to forecast fish stock levels for upcoming months or years.

5. Species-Specific Reports – Provides insights into individual fish species’ population health and habitat risks.

6. Sustainable Fishing Recommendations – Suggests optimal catch limits and fishing zones to avoid over-exploitation.

7. Geospatial Visualization – Interactive maps showing high-stock fishing areas and restricted zones.

8. Regulation Compliance Alerts – Notifies fishermen about fishing bans, quotas, and marine protected areas.

Technology Stack:

• Backend: Node.js / PHP / Java (for API handling, report generation, and user management)

• Frontend: HTML, CSS, Bootstrap, JavaScript (for interactive dashboards)

• Database: MySQL / PostgreSQL with geospatial support (PostGIS)

• Data Science Layer: Python (Pandas, NumPy, Scikit-learn, XGBoost for prediction models)

• Visualization: Leaflet.js / Mapbox for maps, Chart.js / D3.js for charts

• Data Sources & APIs:

  • FAO Fisheries & Aquaculture Data

  • NOAA Fisheries Stock Assessments

  • Copernicus Marine Environment Monitoring Service (CMEMS)

  • IoT-based water quality monitoring devices

Example Use Case:

• A coastal fishing company uses the system to determine which zones will have the highest fish density next season.

• Based on the predictions, the company reduces unnecessary trips, saving fuel and costs.

• The tool recommends fishing during specific moon phases when catch probability is higher.

• Over a year, fish stock levels improve by 12% due to sustainable harvesting strategies.