Date Awarded


Document Type


Degree Name

Doctor of Philosophy (Ph.D.)


Virginia Institute of Marine Science


Jerome P.-Y. Maa


Currently available wind-wave prediction models require a prohibitive amount of computing time for simulating non-linear wave-wave interactions. Moreover, some parts of wind-wave generation processes are not fully understood yet. For this reason accurate predictions are not always guaranteed. In contrast, Artificial Neural Network (ANN) techniques are designed to recognize the patterns between input and output so that they can save considerable computing time so that real-time wind-wave forecast can be available to the navy and commercial ships. For this reason, this study tries to use ANN techniques to predict waves for winter storms and hurricanes with much less computing time at the five National Oceanic and Atmospheric Administration (NOAA) wave stations along the East Coast of the U.S. from Florida to Maine (station 44007, 44013, 44025, 44009, and 41009). In order to identify prediction error sources of an ANN model, the 100% known wind-wave events simulated from the SMB model were used. The ANN predicted even untrained wind-wave events accurately, and this implied that it could be used for winter-storm and hurricane wave predictions. For the prediction of winter-storm waves, 1999 and 2001 winter-storm events with 403 data points had 1998 winter-storm events with 78 points were prepared for training and validation data sets, respectively. In general, because winter-storms are relatively evenly distributed over a large area and move slowly, wind information (u and v wind components) over a large domain was considered as ANN inputs. When using a 24-hour time-delay to simulate the time required for waves to be fully developed seas, the ANN predicted wave heights (r = 0.88) accurately, but the prediction accuracy of zero-crossing wave periods was much less (r = 0.61). For the prediction of hurricane waves, 15 hurricanes from 1995 to 2001 and Hurricane Bertha in 1998 were prepared for training and validation data sets, respectively. Because hurricanes affect a relatively small domain, move quickly, and change dramatically with time, the location of hurricane centers, the maximum wind speed, central pressure of hurricane centers, longitudinal and latitudinal distance between wave stations and hurricane centers were used as inputs. The ANN predicted wave height accurately when a 24-hour time-delay was used (r = 0.82), but the prediction accuracy of peak-wave periods was much less (r = 0.50). This is because the physical processes of wave periods are more complicated than those of wave heights. This study shows a possibility of an ANN technique as the winter-storm and hurricane-wave prediction model. If more winter-storm and hurricane data can be available, and the prediction of hurricane tracks is possible, we can forecast real-time wind-waves more accurately with less computing time.



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