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Storm Classification (ML & Deep Learning)
ML/DL models classifying sea state and storm severity using atmospheric and oceanographic data.
Machine LearningDeep LearningTime SeriesPython
Details
About the project
I approached sea state analysis as a multi-class time-series classification problem instead of regression. Analyzed long-term atmospheric and marine data from Eastern Mediterranean (Antalya) using 36-hour sliding windows. Defined 5 severity categories: Normal, Mild, Moderate, Severe, and Extreme.
Conducted a comprehensive model comparison from classical ML (SVM, Random Forest, XGBoost) to deep learning (1D CNN, PatchTST).
Highlights
Key features
- Temporal evolution is critical for accurate classification
- Nonlinear classifiers essential for complex atmospheric-oceanic interactions
- Class imbalance is a major challenge due to rare extreme events
- PatchTST achieved the best expressiveness-generalization balance
Tech Stack
Tools used
PythonPyTorchPatchTSTCNNscikit-learnMiniROCKET