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Removal of bird-contaminated wind profiler data based on neural networks

✍ Scribed by Ralf Kretzschmar; Nicolaos B. Karayiannis; Hans Richner

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
387 KB
Volume
36
Category
Article
ISSN
0031-3203

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✦ Synopsis

This paper presents the results of a study that relied on trainable neural network classiΓΏers to identify and remove bird-contaminated data from wind measurements recorded by a 1290-MHz wind proΓΏler. A wind proΓΏler is a Doppler radar system measuring the three-dimensional wind ΓΏeld. Migrating birds crossing the radar beam can lead to erroneous wind observations. Bird removal was performed by training conventional feedforward neural networks (FFNNs) and quantum neural networks (QNNs) to identify and remove bird-contaminated data recorded by a 1290-MHz wind proΓΏler. A series of experiments evaluated several sets of input features extracted from wind proΓΏler data, various FFNNs and QNNs of di erent sizes, and criteria employed for identifying birds in wind proΓΏler data.

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