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ThinkMind // INTELLI 2017, The Sixth International Conference on Intelligent Systems and Applications // View article intelli_2017_4_10_60054


Bagged Fuzzy k-nearest Neighbors for Identifying Anomalous Propagation in Radar Images

Authors:
Hansoo Lee
Jonggeun Kim
Suryo Adhi Wibowo
Sungshin Kim

Keywords: Fuzzy k-nearest neighbors; bootstrap aggregating; anomalous propagation; weather prediction.

Abstract:
Several advanced observation devices, such as radiosondes, satellites, and radars, are utilized in practical weather prediction. The weather radar is an essential device because of its broad coverage with excellent resolution. However, the radar inevitably observes meteorologically irrelevant signals. An anomalous propagation echo is a nonprecipitating echo generated by significantly refracted radar beam toward ground or sea surface. In the case, the radar misrecognizes the surface as a meteorological phenomenon. The false observation results may decrease the accuracy of weather prediction result. Therefore, we propose a novel classification method, in this paper, for identifying anomalous propagation echoes in the radar data by combining fuzzy k-nearest neighbors and Hamamoto’s bootstrapping algorithm. By using actual occurrence cases of anomalous propagation, we have confirmed that the proposed method provides good classification results.

Pages: 62 to 67

Copyright: Copyright (c) IARIA, 2017

Publication date: July 23, 2017

Published in: conference

ISSN: 2308-4065

ISBN: 978-1-61208-576-0

Location: Nice, France

Dates: from July 23, 2017 to July 27, 2017

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