FOWD: A Free Ocean Wave Dataset for Data Mining and Machine Learning
| Autor: | Dion Häfner, Johannes Gemmrich, Markus Jochum |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
Computer science FOS: Physical sciences Ocean Engineering Sea state computer.software_genre Machine learning KURTOSIS Data science Physics - Geophysics Wind wave Wave properties ROGUE WAVES Rogue wave Data quality control Data mining computer.programming_language Buoy business.industry Univariate Waves oceanic Probability and statistics Python (programming language) Geophysics (physics.geo-ph) Data processing Physics - Atmospheric and Oceanic Physics Physics - Data Analysis Statistics and Probability Atmospheric and Oceanic Physics (physics.ao-ph) Kurtosis Artificial intelligence business computer Data Analysis Statistics and Probability (physics.data-an) |
| Zdroj: | Hafner, D, Gemmrich, J & Jochum, M 2021, ' FOWD : A Free Ocean Wave Dataset for Data Mining and Machine Learning ', Journal of Atmospheric and Oceanic Technology, vol. 38, no. 7, pp. 1305-1322 . https://doi.org/10.1175/JTECH-D-20-0185.1 |
| Popis: | The occurrence of extreme (rogue) waves in the ocean is for the most part still shrouded in mystery, as the rare nature of these events makes them difficult to analyze with traditional methods. Modern data mining and machine learning methods provide a promising way out, but they typically rely on the availability of massive amounts of well-cleaned data.To facilitate the application of such data-hungry methods to surface ocean waves, we developed FOWD, a freely available wave dataset and processing framework. FOWD describes the conversion of raw observations into a catalogue that maps characteristic sea state parameters to observed wave quantities. Specifically, we employ a running window approach that respects the non-stationary nature of the oceans, and extensive quality control to reduce bias in the resulting dataset.We also supply a reference Python implementation of the FOWD processing toolkit, which we use to process the entire CDIP buoy data catalogue containing over 4 billion waves. In a first experiment, we find that, when the full elevation time series is available, surface elevation kurtosis and maximum wave height are the strongest univariate predictors for rogue wave activity. When just a spectrum is given, crest-trough correlation, spectral bandwidth, and mean period fill this role. |
| Databáze: | OpenAIRE |
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