Classification of Storm Events Using a Fuzzy Encoded Multilayer Perceptron

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Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Year:
2000

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Artificial Intelligence in Medicine

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Abstract

Volumetric radar data are used to detect severe summer storm events but discriminating between storm event types is a challenge due to the high dimensionality and amorphous nature of the data, the paucity of data labeled through an external independent reference test, and the imprecision of the class labels. Two multilayer perceptron architectures are used to discriminate between two types of storm events, hail and tornado. The first architecture uses the original meteorological feature vectors whereas the second transforms some of these features using a method known as fuzzy inter-quartile encoding. Both architectures are benchmarked against linear discriminant analysis. It is shown that the fuzzy encoded multilayer perceptron significantly outperforms the other two methods.