paper
2022
Observations & remote sensing
New model or method
Classical ML (trees, GP, linear, kernels) Journal
2022-06-08
008
Jun 2022
Andrea Trucco, Annalisa Barla, Roberto Bozzano et al.
IEEE Journal of Oceanic Engineering
Random forest regression with hourly compounding predicts wind speed from underwater noise, reaching MAE 0.62 m/s and correlation 0.96.
Compounding Approaches for Wind Prediction From Underwater Noise by Supervised Learning
In one sentence. Random forest regression with hourly compounding predicts wind speed from underwater noise, reaching MAE 0.62 m/s and correlation 0.96.
Labels
- Topic
- Observations & remote sensing
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1109/joe.2022.3162689 OpenAlex:W4285288749
- Added
- 2026-10-22
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