Abstract
In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and minimax probability machines (MPMs) are applied for tornado detection. The last two approaches utilize kernel methods to address non-linearity of the data in the input space. All methods are applied to detect when tornadoes occur, using variables based on radar derived velocity data and month number. Computational results indicate that BNNs are more accurate for tornado detection over a suite of forecast evaluation indices.
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