One-day wave forecasts using buoy data and artificial neural networks

Shreenivas Londhe, Vijay Panchang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The forecasting of wave heights is an essential prerequisite for planning, operation, and maintenance works associated with offshore engineering, navigation, and other activities. The requisite wave height information is currently derived from numerical models which use predicted wind information based on wind-wave relationships. These models sometimes cannot be applied easily at remote locations or on small geographic scales where forcing functions (wind-fields and open ocean boundary conditions) may be unavailable. This paper attempts to forecast waves based on observed wave data only, using the data-driven artificial neural network (ANN) approach. Wave heights with varying lead times of 6 to 24 hours are predicted at five different NDBC buoy locations around the US, viz. two near Alaska (in Prince William Sound), two off the northeast coast (in the Gulf of Maine) and one in the northern Gulf in Mexico (south of Galveston, Texas). Three-layered feed-forward back-propagation networks were used along with the conjugate gradient algorithm. The results show that ANN models perform extremely well at all five locations for the 6 to 12 hour predictions and moderately well for 18 to 24 hours predictions. Online models were also developed using these trained networks and run daily for a period of two months (April - May 2005) to forecast the next day's wave heights. The results of these models also follow the same level of accuracy achieved in testing these networks as mentioned earlier. When large grid-scale modeling is not possible, buoys that measure wave heights provide the requisite wave information. The present work can be viewed as an attempt to enhance the value of buoy data by providing a forecast. This modeling approach will provide a useful tool for forecasting or supplement wave data especially for locations where buoys have been recently installed or where established wave models are difficult to use due to area limitations.

Original languageEnglish
Title of host publicationProceedings of MTS/IEEE OCEANS, 2005
Volume2005
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventMTS/IEEE OCEANS, 2005 - Washington, DC, United States
Duration: 18 Sep 200523 Sep 2005

Other

OtherMTS/IEEE OCEANS, 2005
CountryUnited States
CityWashington, DC
Period18/9/0523/9/05

Fingerprint

Neural networks
Buoys
Backpropagation
Coastal zones
Numerical models
Navigation
Acoustic waves
Boundary conditions
Planning
Testing

ASJC Scopus subject areas

  • Engineering(all)

Cite this

One-day wave forecasts using buoy data and artificial neural networks. / Londhe, Shreenivas; Panchang, Vijay.

Proceedings of MTS/IEEE OCEANS, 2005. Vol. 2005 2005. 1640074.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Londhe, S & Panchang, V 2005, One-day wave forecasts using buoy data and artificial neural networks. in Proceedings of MTS/IEEE OCEANS, 2005. vol. 2005, 1640074, MTS/IEEE OCEANS, 2005, Washington, DC, United States, 18/9/05. https://doi.org/10.1109/OCEANS.2005.1640074
Londhe, Shreenivas ; Panchang, Vijay. / One-day wave forecasts using buoy data and artificial neural networks. Proceedings of MTS/IEEE OCEANS, 2005. Vol. 2005 2005.
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