Learning Networks For Tornado Forecasting:A Bayesian Perspective

Abstract

In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and Bayesian support vector machines (BSVMs) are applied for tornado forecasting. The last two approaches utilize kernel methods to address nonlinearity of the data in the input space. All methods are applied to forecast when tornadoes occur, using variables based on radar derived velocity data and month number. Computational results indicate that Bayesian methods have a higher skill level compared to ANNs and SVMs for a tornado forecast system.