Artificial Neural Network (ANN) models were trained and optimized to predict water temperatures based on previous water temperatures and previous and forecasted air temperatures. Other inputs considered include along shore and across shore wind squared, water level history, and tidal forecasts. As the performance of the ANN varies depending on the length of the forecast, both a short-term model (3 and 12 hour forecasts) and a long-term model (24 and 48 hour forecasts) were developed and optimized. The inputs that maximize performance in both models include previous water temperatures and previous and forecasted air temperatures. Adding a time stamp reduces the error in the short-term model but does not significantly affect the long-term model. Previous water temperatures from a nearby location decrease the error for long-term forecasts but do not have a similar effect on short-term forecasts. Different ANN topologies are tested and compared. The optimized ANN design is then compared to the conclusions of multivariate statistical analysis in an attempt to determine whether statistical analysis aids in the development and optimization of the ANN. Finally, the performance and operational applicability of the model are discussed.
Supplementary URL: http://lighthouse.tamucc.edu/Main/RobynBall