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  • Tide modelling using support vector machine regression

    Author(s)
    Okwuashi, Onuwa
    Ndehedehe, Christopher
    Griffith University Author(s)
    Ndehedehe, Christopher E.
    Year published
    2017
    Metadata
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    Abstract
    This research explores the novel use of support vector machine regression (SVMR) as an alternative model to the conventional least squares (LS) model for predicting tide levels. This work is based on seven harmonic constituents: M2, S2, N2, K2, K1, O1 and P1. The SVMR is modelled with four kernel functions: linear, polynomial, Gaussian radial basis function and neural. The computed r-square and root mean square error for the linear, polynomial, Gaussian radial basis function and neural SVMR kernels as well the LS indicate a strong correlation between the observed and predicted tides. But for the linear kernel the results of ...
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    This research explores the novel use of support vector machine regression (SVMR) as an alternative model to the conventional least squares (LS) model for predicting tide levels. This work is based on seven harmonic constituents: M2, S2, N2, K2, K1, O1 and P1. The SVMR is modelled with four kernel functions: linear, polynomial, Gaussian radial basis function and neural. The computed r-square and root mean square error for the linear, polynomial, Gaussian radial basis function and neural SVMR kernels as well the LS indicate a strong correlation between the observed and predicted tides. But for the linear kernel the results of all the kernels are slightly better than the LS. The statistical tests of the difference between the observed tide and the LS and SVMR predicted tides and between the LS and SVMR predicted tides are insignificant at the 95% confidence level.
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    Journal Title
    Journal of Spatial Science
    Volume
    62
    Issue
    1
    DOI
    https://doi.org/10.1080/14498596.2016.1215272
    Subject
    Physical geography and environmental geoscience
    Geomatic engineering
    Geospatial information systems and geospatial data modelling
    Publication URI
    http://hdl.handle.net/10072/378534
    Collection
    • Journal articles

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