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Atmosphere 2017, 8(1), 11; doi:10.3390/atmos8010011

Ensemble Classification for Anomalous Propagation Echo Detection with Clustering-Based Subset-Selection Method

1
Department of Electrical and Computer Engineering, Pusan National University, Busan 46241, Korea
2
School of Electrical and Computer Engineering, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Guifu Zhang
Received: 31 October 2016 / Revised: 6 January 2017 / Accepted: 9 January 2017 / Published: 13 January 2017
(This article belongs to the Special Issue Radar Meteorology)
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Abstract

Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated atmospheric conditions. Anomalous propagation echoes should be removed because they make weather forecasting difficult. In this paper, we suggest an ensemble classification method based on an artificial neural network and a clustering-based subset-selection method. This method allows us to implement an efficient classification method when a feature space has complicated distributions. By separating the input data into atomic and non-atomic clusters, each derived cluster will receive its own base classifier. In the experiments, we compared our method with a standalone artificial neural network classifier. The suggested ensemble classifier showed 84.14% performance, which was about 2% higher than that of the k-means clustering-based ensemble classifier and about 4% higher than the standalone artificial neural network classifier. View Full-Text
Keywords: anomalous propagation echo; ensemble classifier; clustering; subset-selection; radar data analysis anomalous propagation echo; ensemble classifier; clustering; subset-selection; radar data analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lee, H.; Kim, S. Ensemble Classification for Anomalous Propagation Echo Detection with Clustering-Based Subset-Selection Method. Atmosphere 2017, 8, 11.

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