An Adaptive Neuro-Fuzzy Inference System for Seasonal Forecasting of Tropical Cyclones Making Landfall along the Vietnam Coast

摘要

The regression is a causal forecasting method that fits curves to the entire data set to minimize the forecasting errors. It should be noted that the linear statistic-based regression models does not support nonlinear in forecasting. According to literature, Bayesian- and Neural Network-based regression for seasonal typhoon activity forecasting is more effective than the traditional regression models. In this paper, a conjunct space cluster-based adaptive neuro-fuzzy inference system (ANFIS) is applied for seasonal forecasting of tropical cyclones making landfall along the Vietnam coast. The experimental results indicated that the conjunct space cluster-based ANFIS for seasonal forecasting of tropical cyclones is an effective approach with high accuracy.