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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/25462

Title: Wave measurements by pressure transducers using artificial neural networks
Authors: Jen-Chih Tsai;Cheng-Han Tsai
Contributors: NTOU:Department of Marine Environmental Informatics
Keywords: Artificialneuralnetwork;Pressure transfer function;Wave parameters;Pressure gauge;Ocean wavemeasurement
Date: 2009-11
Issue Date: 2011-10-20T08:23:18Z
Publisher: Ocean Engineering
Abstract: abstract:Underwater ultrasonic acoustic transducers are frequently used in ocean wave measurements, as they measure surface level using acoustic waves. However, their effectiveness could be severely affected in rough sea conditions, in which breaking waves generated bubbles interfere with their acoustic signals. Therefore, when the seas are rough, one often has to rely on pressure transducer, which is generally used as a back-up for the acoustic wave gauge. Then a pressure transfer function is used to obtain the surface wave information. Alternatively, this study employed the artificial neural network to convert the pressure signal into significant wave height, significant wave period, maximum wave height and spectral peakedness parameter using data obtained from various water depths. The results showed that the wave parameters obtained from the artificial neural network were significantly closer to that obtained by the acoustic measurements than that by using linear pressure transfer function for water depth larger than 20 m. Moreover, for a given water depth, the wave height estimated by the network model from pressure data were not as good as that by linear wave theory for large wave height (above 4 m in significant wave height in this study). This can be improved if the training data set has more records of large wave height.
Relation: 36(15-16), pp.1149–1157
URI: http://ntour.ntou.edu.tw/handle/987654321/25462
Appears in Collections:[海洋環境資訊系] 期刊論文

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