Wavelet fuzzy classification for detecting and tracking region outliers in meteorological data

Abstract

In this paper, a wavelet fuzzy classification approach is proposed to detect and track region outliers in meteorological data. First wavelet transform is applied to meteorological data to bring up distinct patterns that might be hidden within the original data. Then a powerful image processing technique, edge detection with competitive fuzzy classifier, is extended to identify the boundary of region outlier. After that, to determine the center of the region outlier, the fuzzy-weighted average of the longitudes and latitudes of the boundary locations is computed. By linking the centers of the outlier regions within consecutive frames, the movement of a region outlier can be captured and traced. Experimental evaluation was conducted on a real-world meteorological data to examine the effectiveness of the proposed approach. This work will help discover interesting and implicit information for large volume of meteorological data.