Veneros-Castro, Anthony

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A neural network approach to predict hurricane intensity in the North Atlantic basin
(2004) Veneros-Castro, Anthony; Ramírez-Beltrán, Nazario D.; College of Engineering; Hernández, William; Gonzáles, Jorge E.; Vasquez Espinosa, Ramon E.; Department of Industrial Engineering; Ierkic-Vidmar, Mario
Upper air information and artificial neural networks (ANN) are used to predict hurricane intensity in the North Atlantic basin. Competitive neural network is used to identify analog storms to the current hurricane. Once the analog hurricanes are identified the historical NCEP reanalysis data are used along of each storm tracks to develop a set of climatology, persistence and synoptic variables. Persistence, climatological and synoptic observations of the analog hurricanes and the current storm are combined to create a training set which is used to generate nonlinear transformations and an optimization algorithm is used to identify the variables that are best correlated with storm intensity. The best variables obtained from the optimization algorithm are used to train a neural network which used Levenberg-Marquardt algorithm as a learning rule. Preliminary results show that the proposed prediction scheme is a potential tool to increase the accuracy in predicting hurricane intensity.