A neural network regression model for tropical cyclone forecast

In recent years a large amount of literature has evolved on the usage of artificial neural network (ANNs) for weather forecasting, particularly because of ANNs' ability to model an unspecified nonlinear relationship of various meteorological variables. In this paper we proposed a dynamic competitive neural network classifier to predict the maximum potential intensity (MPI) of a given tropical cyclone, based on a 10-year period of Western North Pacific tropical cyclones and monthly Mean Sea Surface Temperature (MSST). A procedure to select most significant-correlated attributes of tropical cyclones is designed to for fast and accurate neural network training. A Binary Trigger is adopted to adjust the structure of the network layers. To justify the performance, we carry out a set of experiments to prove that our proposed model is promising.