Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks
Tienfuan Kerh, Shin-Hung Wu · International Journal of Artificial Intelligence & Applications · 2017
Forecasting of a typhoon moving path may help to evaluate the potential negative impacts in the neighbourhood areas along the moving path.This study proposed a work of using both static and dynamic neural network models to link a time series of typhoon track parameters including longitude and latitude of the typhoon central location, cyclonic radius, central wind speed, and typhoon moving speed.Based on the historical records of 100 typhoons, the performances of neural network models are evaluated from the indices of a correlation coefficient and a mean square error.The dynamic model or the so-called nonlinear autoregressive network with the use of a moving average method proved to forecast the ten types of typhoon moving path more effectively in Taiwan region.The new and simply approach developed in this study for solving studied typhoon cases may be applicable to other areas of interest worldwide..