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RECENT DATA DRIVEN METHODS AND APPLICATIONS IN COASTAL AND HYDROLOGIC DATAANALYSIS

ABSTRACT An introduction and review of some of the recent data driven methods used in coastal and hydrologic engineering to analyze random data are presented. The methods considered are locally weighted projection regression, model tress, genetic programming, and support vector regression, apart from the well known artificial neural networks. It is found that a distinct superiority of one method over the other is not commonly observed; however some methods may do relatively better under certain conditions such as estimation of higher magnitudes of the variable, prediction over longer intervals, very high input variability, and small sample size. The lack of application of uniform error criteria often prevents comparison of results across studies made by different investigators. The potential of the technique of locally weighted projection regression, that appears to be equally good as other methods discussed in this paper, is yet to be sufficiently tapped in coastal and hydrologic applications. It is felt that the availability of free software for a given method can invite a large number of its applications by enthusiastic investigators.