id: 06115833
dt: a
an: 06115833
au: Wang, Jin-Dong; Sun, Xiao-Gong
ti: Analyzing and predicting interannual variability in nearshore topographic
    field on large scales by EOF and ANN.
so: Cao, Bing-Yuan (ed.) et al., Quantitative logic and soft computing 2010.
    Vol. 2. Proceedings of the 2nd international conference (QL \& SC
    2010), Xiamen, China, October 22‒25, 2010. Berlin: Springer (ISBN
    978-3-642-15659-5/pbk; 978-3-642-15660-1/ebook). Advances in
    Intelligent and Soft Computing 82, 603-612 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: EOF; ANN; interannual variability; nearshore topography; the yellow river
    delta
ci: 
li: doi:10.1007/978-3-642-15660-1_61
ab: Summary: Empirical Orthogonal Functions (EOF) and Artificial Neural Network
    (ANN) are performed for investigating and predicting interannual
    variability in nearshore topographic field around the Yellow River
    Delta. EOF and ANN are particularly effective at reproducing the
    observed topographic features around either modern or historical river
    mouths where the nearshore topography has experienced significantly
    intense interannual changes during the last thirty decades. In general,
    the coastal land around the modern river mouths has extended toward the
    sea and the seafloor has been elevated due to accumulation of a large
    amount of sediment transported by the modern Yellow River. On the other
    hand, the coast and the seafloor around the historical river mouths
    have been eroded quickly by tidal current, waves and storms due to lack
    of sediment supply. The observed spatial patterns of nearshore
    topography are well captured qualitatively and quantitatively by
    dominant eigenvectors of EOF. The EOF principal components indicating
    temporal variation in dominant eigenvectors are effectively fitted and
    predicted by using the observed river-related data as the input to ANN.
    The topographic fields either for the “missing” years or for the
    “future” years can then be estimated by linear combination of fixed
    dominant eigenvectors and fitted principal components. As a result, the
    fitting and predicting errors reach as low as 2.9\% and 5.6\%,
    respectively.
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