Paper Type

Master's Thesis


College of Computing, Engineering & Construction

Degree Name

Master of Science in Civil Engineering (MSCE)



NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Dr. Cigdem Akan

Second Advisor

Dr. Adel ElSafty

Third Advisor

Dr. Beyza Aslan

Department Chair

Alan Harris


Extreme wind events, such as hurricanes and tropical storms, have devastating impacts on infrastructure, economy, and public safety in Florida. Traditional predictive methods, linear regression, logistic regression, and autoregressive integrated moving-average (ARIMA) models have limitations in accounting for non-stationarity and nonlinearity in meteorological data, leading to less accurate predictions. The primary objective of this study is to leverage the capabilities of deep learning algorithms, specifically, Feedforward Neural Networks (FFNNs), to develop a predictive model that outperforms traditional predictive methods in forecasting extreme wind events in Florida. The deep learning model was trained, validated, and tested using historical wind speed data obtained from 34 stations located throughout Florida. The performance of the model was evaluated and compared to the Support Vector Regression model (SVR) using measures such as Mean Square Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Results indicate that the feedforward neural network (FFNN) model achieved higher prediction accuracy than the Support Vector Regression (SVR) model. Deep learning offers a promising avenue for more accurate, adaptable, and comprehensive models for predicting extreme wind events in Florida.

Available for download on Sunday, January 12, 2025