1. Wave height prediction in the Apostle Islands
Michael Meyer

2. Apostle Islands – Sea Caves site
Popular site with recreational kayakers
Kayaker launching point

3. Project outline
Goal: Prediction of current waveheights at the Sea Caves sites
Approach:
Apply weather and buoy data to predict wave heights using a neural network
Use committee of machines approach to create waveheight prediction using neural network output and the output of two continuously running wave models

4. Wave Prediction Approach
Input data:
Weather data from nearby weather stations and a nearby buoy
Model waveheight prediction from Great Lakes Coastal Forecasting System
Model waveheight prediction from SWAN wave model of the Apostle Islands run at UW
Target data: Waveheight measurements from sensor placed at the site during summers
Neural network inputs
Committee Machine inputs

5. Wave height prediction data
Weather station data utilized
Wind speed
Wind direction
Buoy data utilized
Wave height
Air temperature
Water temperature
Weather station
Wave buoy
Eagle Island station
West Superior buoy
Sea Caves site
Port Wing station (inconsistent data)

6. Wind direction
Wind direction has a significant influence on wave characteristics
Data broken into 6 wind direction categories (as measured at Eagle Island station) for neural network construction
Training conducted separately for each wind direction

7. Data records division

8. Neural network construction
Individual network trained for each wind direction (effectively a committee)
13 inputs composed of wind speeds up to four hours prior, wave buoy wave height, difference between water and air temperature, change in wind direction over the past two hours
Empirically found that a 5 neuron, single layer sigmoidal network was the most effective
0° to 60° network
60° to 120° network
Weather data inputs
Neural network output …
300° to 360° network

9. Committee Machine Construction
Expert 2 – GLERL model
Expert 3 – My neural net committee (each trained for subset of wind directions)
Expert 1 – SWAN model
Gating network – linear combination, weights inversely proportional to MSE of method in training set
Machine output

10. Results – evaluated using root means square error
*Units are in feet

11. Conclusion
Individual neural network outputs were generally not an improvement over the model outputs
Committee machine allowed for an improvement over individual models for some wind conditions and when evaluating the data on the whole