Bayesian Neural Networks Based Bootstrap Aggregating for Tropical Cyclone Tracks Prediction in South China Sea

Accurate forecasting of Tropical Cyclone Track (TCT) is very important to cope with the associated disasters. The main objective in the presented study is to develop models to deliver more accurate forecasts of TCT over the South China Sea (SCS) and its coastal regions with 24 h lead time. The model proposed in this study is a Bayesian Neural Network (BNN) based committee machine using bagging (bootstrap aggregating). Two-layered Bayesian neural networks are employed as committee members in the committee machine. Forecast error is measured by calculating the distance between the real position and forecast position of the tropical cyclone. A decrease of 5.6 km in mean forecast error is obtained by our proposed model compared to the stepwise regression model, which is widely used in TCTs forecast. The experimental results indicated that BNN based committee machine using bagging for TCT forecast is an effective approach with improved accuracy.

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