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  • Neural network for the prediction and supplement of tidal record in Taichung Habor, Taiwan

    Author(s)
    Lee, TL
    Tsai, CP
    Jeng, DS
    Shieh, RJ
    Griffith University Author(s)
    Jeng, Dong-Sheng
    Year published
    2002
    Metadata
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    Abstract
    Accurate tidal prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. The harmonic tidal level is conventionally used to predict tide levels. However, determination of the tidal components using the spectral analysis requires a long-term tidal level record (more than one year [Handbook of coastal and ocean engineering 1 (1990) 534]). In addition, calculating the coefficients abbreviated of tide component using the least-squares method also requires a large database of tide measurements. This paper presents an application of the artificial neural network ...
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    Accurate tidal prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. The harmonic tidal level is conventionally used to predict tide levels. However, determination of the tidal components using the spectral analysis requires a long-term tidal level record (more than one year [Handbook of coastal and ocean engineering 1 (1990) 534]). In addition, calculating the coefficients abbreviated of tide component using the least-squares method also requires a large database of tide measurements. This paper presents an application of the artificial neural network for predicting and supplementing the long-term tidal-level using the short term observed data. On site, tidal-level data at Taichung Harbor in Taiwan will be used to test the performance of the artificial neural network model. The results show that the tidal levels over a long duration can be efficiently predicted or supplemented using only a short-term tidal record
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    Journal Title
    Advances in Engineering Software
    Volume
    33
    Issue
    6
    DOI
    https://doi.org/10.1016/S0965-9978(02)00043-1
    Subject
    Information and computing sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/6662
    Collection
    • Journal articles

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