Journal article
Detection of anomalous propagation echoes in weather radar data using neural networks
IEEE Transactions on Geoscience and Remote Sensing, Vol.37(1), pp.287-296
01/1999
DOI: 10.1109/36.739163
Abstract
The authors investigate a neural network-based methodology for detection of the anomalous propagation (AP) radar echo. The methodology is devised to cope with the situations when only single scan data are available. The output of the procedure is quantified in four classes corresponding to the upper limits of 25, 50, 75, and 100% of AP echo per scan. The high dimension of the input data space is reduced by feature extraction based on physical considerations. Fractal based, statistical, and wavelet analyses are performed, and their characteristics are used as features. A feedforward neural network is used for classification in the four classes, with a fuzzy strategy used in the network training. The authors test the methodology on real data and make a comprehensive assessment of the procedure's accuracy based on cross validation.
Details
- Title: Subtitle
- Detection of anomalous propagation echoes in weather radar data using neural networks
- Creators
- M Grecu - Inst. of Hydraulic Res., Iowa Univ., Iowa City, IA, USAW.F Krajewski
- Resource Type
- Journal article
- Publication Details
- IEEE Transactions on Geoscience and Remote Sensing, Vol.37(1), pp.287-296
- Publisher
- IEEE
- DOI
- 10.1109/36.739163
- ISSN
- 0196-2892
- eISSN
- 1558-0644
- Language
- English
- Date published
- 01/1999
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9983992073802771
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