About Workshop
This 3-day hands-on workshop focuses on the application of AI for marine ecosystem monitoring, resource management, and conservation efforts. Participants will learn to use satellite data, oceanographic sensors, and machine learning techniques to monitor biodiversity, predict oceanic patterns, manage fisheries, and optimize the sustainability of marine protected areas (MPAs). Practical exercises will include preprocessing marine data, developing predictive models for ocean phenomena and fishing zones, and creating decision-support tools for conservation management.
Aim
To equip participants with the skills to apply AI and machine learning for marine ecosystem monitoring, sustainable fishing practices, and effective marine conservation area management.
What Participants Will Learn
- Understand AI applications in marine biodiversity monitoring, coral reefs, and ecosystem health.
- Learn how AI can optimize resource allocation and management for fisheries and conservation.
- Analyze marine data from various sources (satellite data, oceanographic sensors) for ecosystem analysis.
- Build machine learning models to predict oceanic phenomena and sustainable fishing zones.
- Apply AI to manage marine protected areas (MPAs), track biodiversity, and improve conservation strategies.
- Develop decision-support tools using AI to inform policy and manage marine ecosystems sustainably.
Structure
🌊 Day 1: Introduction to Ocean Data Analytics & Marine Monitoring
- Overview of sustainable marine resource management and its global significance
- Role of AI and data analytics in ocean and coastal monitoring
- Introduction to major ocean data sources: satellites, buoys, sensors, research vessels, and Argo floats
- Understanding key marine variables: sea surface temperature, salinity, chlorophyll-a, dissolved oxygen, and ocean currents
- Introduction to oceanographic data formats such as CSV, NetCDF, GeoTIFF, and GeoJSON
🐟 Day 2: Machine Learning for Marine Ecosystem & Water Quality Prediction
- Introduction to machine learning applications in marine and coastal sciences
- Marine water quality classification and environmental risk prediction
- AI-based detection of harmful algal blooms, pollution, and ecosystem stress
- Data preprocessing, missing-value treatment, feature engineering, and normalization
- Machine learning models: Random Forest, Decision Trees, Gradient Boosting, and clustering
- Model evaluation using accuracy, precision, recall, RMSE, and feature importance
🛰️ Day 3: GeoAI, Marine Resource Mapping & Future Trends
- Integration of satellite imagery, sensor data, and oceanographic observations
- GeoAI applications for fisheries, aquaculture, biodiversity, and coastal-zone management
- Mapping marine pollution, plastic waste, oil spills, and ecosystem-risk hotspots
- AI-based habitat suitability and sustainable fishing-zone identification
- Marine decision-support systems and resource-management dashboards
- Advanced AI techniques: LSTM, CNN, Transformers, and multimodal data fusion
- Emerging trends in ocean analytics: marine digital twins, autonomous vehicles, drones, IoT sensors, and real-time forecasting
- Ethical considerations, data reliability, uncertainty, and responsible use of marine AI
Important Dates
Registration Ends
4:30 PM
Workshop Dates
2026-07-30
5:30 PM
5:30 PM
What You Will Gain
Outcomes
- Preprocess marine ecosystem data (biodiversity, fish populations) for machine learning analysis.
- Develop machine learning models to predict oceanic patterns (currents, temperatures) and sustainable fishing zones.
- Apply AI to assess fish stocks and prevent overfishing using ocean data.
- Build AI-driven decision-support tools for marine conservation, including biodiversity tracking and sustainability metrics.
- Use AI and data analytics to optimize the management of marine protected areas and inform policy decisions.
Who Should Attend
- Marine biologists, environmental scientists, fisheries managers, data scientists, and conservationists.
- Basic understanding of machine learning and marine ecology is helpful but not required.
- Participants should have an interest in using AI to improve marine ecosystem health and resource management.