AI for Ocean Currents & Tsunami Early-Warning
From waves to warnings: building AI pipelines for ocean risk.
About This Course
AI for Ocean Currents & Tsunami Early-Warning is a 3-day, hands-on workshop that blends coastal physics with modern ML to turn ocean data into actionable alerts. You’ll clean and grid buoy/HF-radar/altimetry data, run data assimilation, train compact models (PINNs, FNOs, encoder–decoders) for short-term forecasts, and quantify uncertainty with ensembles and conformal prediction. By the end, you’ll detect tsunami-like anomalies, estimate ETAs with confidence bands, and wire a mini operational pipeline—from ingest and QC to calibrated alerts and a one-page situation report.
Aim
Equip participants to build an end-to-end, physics-aware AI workflow that converts ocean observations into reliable, uncertainty-calibrated forecasts and tsunami early-warning alerts—covering data ingest/QC, data assimilation, ML forecasting (PINNs/FNOs/transformers), and operational alert design.
Course Objectives
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Grasp core coastal/tsunami physics (shallow-water, bathymetry).
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Ingest, QC, align, and grid buoy/HF-radar/altimetry data.
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Benchmark baselines vs ML (PINNs, FNOs, encoder–decoders/SSMs).
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Apply lightweight data assimilation and read analysis increments.
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Produce short-horizon forecasts with calibrated uncertainty.
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Detect tsunami-like anomalies and estimate ETA with bands.
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Evaluate skill (CRPS, PR/ROC, cost–loss) and set alert thresholds.
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Wire an ops pipeline and generate a one-page situation report.
Syllabus
📅 Day 1 – Introduction to AI for Ocean Currents & Sensing
- Ocean Observations 101: Buoys/DART, HF radar, satellite altimetry (SSH)
- Data Formats & Access: NetCDF/Zarr, coordinates, gridding to coastal domains
- Physical Basics: shallow-water intuition, bathymetry, boundaries
- ML Landscape: baselines (persistence/AR), PINNs vs Neural Operators (FNO)
- Hands-on: Access small buoy/HF radar/altimetry samples; run quick QC (spike/flatline/time); align & grid into a tidy dataset
- Hands-on: Train a tiny PINN on synthetic wave probes; run a minimal FNO for +6 h surface-current forecast vs persistence
📅 Day 2 – Forecasting, Data Assimilation & Uncertainty
- Data Assimilation Essentials: EnKF/3D-Var intuition, increments, observation error
- Sequence Models for Marine Time Series: encoder–decoder transformers, SSMs; masking & missing data
- Uncertainty & Calibration: ensembles, heteroscedastic outputs, conformal prediction; coverage & CRPS
- Hands-on: Assimilate an HF-radar snapshot into yesterday’s state and visualize analysis increments
- Hands-on: Build a compact encoder–decoder for buoy streams (+24 h horizon) and add conformal bands; verify nominal coverage
📅 Day 3 – Tsunami Detection, ETA & Operational Alerts
- Tsunami Detection from DART/Tide Gauges: detrending, anomaly scoring, robustness to noise/clock drift
- ETA Estimation & Communication: uncertainty bands and practical reporting
- Operational Workflow: ingest → QC → assimilation → forecast → UQ → post-process → publish
- Alert Design: precision–recall vs ROC, class imbalance, cost–loss analysis, tiering (Advisory/Watch/Warning)
- Hands-on: Detect tsunami-like anomalies and estimate ETA with uncertainty
- Hands-on: Wire a mini pipeline (configs/CLI) to produce alerts; pick thresholds from PR curves and export a one-page situation report (maps, bands, alert level)
Who Should Enrol?
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Coastal & ocean modelers, met-ocean analysts, and early-warning/DRM teams
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Data scientists/ML engineers in public agencies, NGOs, or blue-economy startups
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Graduate/advanced undergraduate students in Ocean/Climate/EE/CS/ME
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Researchers working with buoy, HF-radar, satellite altimetry, or tide-gauge data
Course Outcomes
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QC, align, and grid buoy/HF-radar/altimetry data (NetCDF/Zarr).
-
Train baselines + physics-aware ML (PINNs, FNOs, encoder–decoders).
-
Assimilate observations (EnKF intuition) and visualize increments.
-
Deliver +6 h surface-current & +24 h buoy forecasts with calibrated uncertainty.
-
Detect tsunami-like anomalies and estimate ETA with confidence bands.
-
Evaluate skill (CRPS, PR/ROC, cost–loss) and set tiered alert thresholds.
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Ship a minimal ops pipeline (ingest→QC→DA→forecast→UQ→publish) and a one-page situation report.
Course fee
Learners in India
₹2,499
Learners outside India
$50
- Video lectures
- Study material
- Certificate after completion
- 365-day access
The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Frequently asked questions
How is this course taught?
Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.
How long do I have access?
365 days from your registration, on your NanoSchool dashboard.
Do I get a certificate?
Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
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