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From http://cdip.ucsb.edu: The Coastal Data Information Program (CDIP) is a research group at Scripps Institution of Oceanography that monitors coastal waves and nearshore sand levels on regional scales. CDIP maintains a network of optimally-placed, directional wave buoys from San Diego to Eureka. The buoy measurements are used to initialize a high spatial resolution (100m x 100m) linear spectral wave propagation model. The resulting hourly hindcasts and nowcasts of CA coastal wave conditions have a level of accuracy that is not possible with more traditional wind-wave generation models that are initialized with modeled wind fields.
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Regression-Based Networked Virtual Buoy Model for Offshore Wave Height Prediction
Accurate wave height measurements are critical for offshore wind farm operations, marine navigation, and environmental monitoring. Wave buoys provide essential real-time data; however, their reliability is compromised by harsh marine conditions, resulting in frequent data gaps due to sensor failures, maintenance issues, or extreme weather events. These disruptions pose significant risks for decision-making in offshore logistics and safety planning. While numerical wave models and machine learning techniques have been explored for wave height prediction, most approaches rely heavily on historical data from the same buoy, limiting their applicability when the target sensor is offline. This study addresses these limitations by developing a virtual wave buoy model using a network-based data-driven approach with Random Forest Regression (RFR). By leveraging wave height measurements from surrounding buoys, the proposed model ensures continuous wave height estimation even in the case of malfunctioning physical sensors. The methodology is tested across four offshore sites, including operational wind farms, evaluating the sensitivity of predictions to buoy placement and feature selection. The model demonstrates high accuracy and incorporates a k-nearest neighbors (kNN) imputation strategy to mitigate data loss. These findings establish RFR as a scalable and computationally efficient alternative for virtual sensing, thereby enhancing offshore wind farm resilience, marine safety, and operational efficiency.
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- Award ID(s):
- 2230630
- PAR ID:
- 10662992
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Journal of Marine Science and Engineering
- Volume:
- 13
- Issue:
- 4
- ISSN:
- 2077-1312
- Page Range / eLocation ID:
- 728
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/jmse13040728
