Introduction to the Course

The AI for Ocean Currents & Tsunami Early Warning Course teaches learners how artificial intelligence is used to monitor ocean currents, predict tsunamis, and support early warning systems. Ideal for environmental scientists, data analysts, and AI enthusiasts, this course provides practical skills to apply AI in oceanography, disaster management, and climate monitoring, ensuring real-world impact and societal relevance.

Course Objectives

By the end of the course, participants will be able to:

• Understand core coastal and tsunami physics, including shallow-water dynamics and bathymetry.

• Ingest, QC, and align ocean observation data from sources like buoys, HF-radar, and satellite altimetry.

• Benchmark traditional baselines (persistence, AR) against machine learning models like PINNs and FNOs.

• Apply lightweight data assimilation methods (e.g., EnKF, 3D-Var) to improve forecasts.

• Produce short-horizon forecasts with calibrated uncertainty (P10/P50/P90 outputs).

• Detect tsunami-like anomalies and estimate ETA with uncertainty bands.

• Evaluate model performance using metrics like CRPS, PR/ROC, and cost–loss analysis.

• Build and operationalize a mini pipeline for tsunami forecasting and alert generation, complete with a one-page situation report.

What Will You Learn (Modules)

Module 1: Introduction to Ocean Currents and Tsunami Science

• Overview of ocean currents: surface currents, deep-water circulation, and tides.

• Fundamentals of tsunami generation, propagation, and coastal impact.

• Case studies: 2004 Indian Ocean tsunami, 2011 Japan tsunami, Pacific warning systems.

Module 2: Ocean Data Collection and Monitoring Systems

• Ocean data sources: buoys, satellites, sonar, and seismic sensors.

• Real-time data acquisition for ocean and seismic monitoring.

• Challenges in ocean data quality, latency, and reliability.

Module 3: AI for Ocean Current Analysis

• Machine learning models for analyzing ocean current patterns.

• AI-based anomaly detection in sea-level and pressure data.

• Predicting ocean behavior using historical and real-time datasets.

Module 4: AI for Tsunami Prediction and Early Warning

• AI models for tsunami detection and early warning signals.

• Integrating seismic and ocean sensor data using AI.

• Risk assessment and alert generation for coastal regions.

Module 5: Visualization and Decision Support Systems

• Visualizing ocean data using maps, graphs, and dashboards.

• AI-driven decision support for emergency response planning.

• Communicating early warnings to authorities and communities.

Final Project: AI-Based Tsunami Early Warning System

• Design and develop an AI-powered system for tsunami early detection.

• Integrate ocean sensor data, AI prediction models, and alert logic.

• Present results with simulations, visual outputs, and technical documentation.

Who Should Take This Course?

• Oceanography and environmental science professionals
• Data scientists and AI engineers interested in environmental applications
• Researchers in climate monitoring and disaster prediction
• Career switchers entering AI for environmental monitoring
• Technology enthusiasts passionate about ocean science and AI

Job Oppurtunities

Graduates of this course can pursue roles such as:

• AI Ocean Data Analyst
• Tsunami Early Warning Specialist
• Environmental AI Researcher
• Climate Risk Modeler
• Remote Sensing Analyst
• Disaster Management Technology Consultant

Why Learn With Nanoschool?

• Expert-led training from AI and oceanography professionals
• Practical, hands-on learning with real ocean data and predictive models
• Industry-relevant curriculum aligned with environmental monitoring and disaster management needs
• Career support to help you grow in AI and environmental roles

Key outcomes of the course

By completing this course, you will:

• Build AI models to analyze ocean currents and detect anomalies
• Develop predictive early warning systems for tsunamis
• Apply AI to real-world ocean monitoring and disaster preparedness projects
• Enhance career growth in environmental AI and disaster management fields
• Gain practical experience with sensors, simulations, and data visualization