The present paper looks at algorithms to be implemented in the software of BPRs (bottom pressure recorders ) for the automatic, real-time detection of a tsunami within recorded signals. The structure of an algorithm based on the use of an ANN (artificial neural network (ANN) is presented and compared to the one developed under the DART (Deep-ocean Assessment and Reporting of Tsunamis) program run by the U.S. NOOA (National Oceanic and Atmospheric Administration ). The performance and efficiency of the two algorithms are compared using both synthetic and actually measured time series. Results show that an improvement in detection performance can be obtained by using the ANN algorithm.


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    Title :

    An ANN algorithm for automatic, real-time tsunami detection in deep-sea level measurements


    Additional title:

    Ein Algorithmus eines künstlichen neuronalen Netzes für die automatische Echtzeiterkennung eines Tsunami bei Messungen des Tiefseemeeresspiegels


    Contributors:

    Published in:

    Ocean Engineering ; 35 , 5-6 ; 572-587


    Publication date :

    2008


    Size :

    16 Seiten, 22 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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