AN EXPERIMENTAL STUDY ON ARTIFICIAL NEURAL NETWORK FORECASTING MODELS OF TROPICAL CYCLONE PATH
Teng Wei-ping · Journal of Tropical Meteorology · 2004
Based on BP, LM, RBF artificial neural network with a term of momentum, forecasting models for tropical cyclone path of 36, 48, 60 and 72 hours are set up, and run with 100 independent samples. The results show that the models with good fitting generally produce bad forecast. The keys to avoid this embarrassing situation are proper parameters for network structure, corresponding algorithm and suitable predictors. In view of the fact that artificial neural network models lack the mechanism of automatic predictor adaptation, an algorithm of stepwise predictor adaptation of RBF model is proposed in this study. The comparison with the other models shows that the suggested algorithm is worth to be tried in routine forecasting operation.