Design of meteorological pattern classification system based on FCM-based radial basis function neural networks using meteorological radar data

Abstract

In this study, precipitation and non-precipitation pattern classification of meteorological radar data is realized with the use of a neuro-fuzzy classifier. Structure expression of meteorological radar data information is analyzed and used to effectively classify precipitation and non-precipitation patterns. Various combinations of input variables for designing pattern classifier are considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data. Pattern classifier is designed by involving essential input variables producing the best performance (classification) of the classifier. The proposed architecture is designed by using FCM-based radial basis function neural network. Two components of classifier such as event classifier part and echo classifier part are designed. In the event classifier part, the pattern classifier identifies precipitation and non-precipitation data. As precipitation data information identified by the event classifier could also involve non-precipitation data information, echo classifier is used to carry out further discrimination processes. The performance of the proposed classifier is evaluated and analyzed in comparison with the existing approaches.