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dc.contributor.author Gamage, NPD
dc.date.accessioned 2019-05-21T07:58:48Z
dc.date.available 2019-05-21T07:58:48Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/14354
dc.description.abstract Canonical Correlation is an additional procedure for assessing the relationship between variables. Canonical Correlation permits one to establish the interrelations that may exist between two groups of variables, by identifying the linear combinations of the variables of the first group that are most correlated to some linear combinations of the variables of the second group. In regional flood frequency analysis, canonical correlation can be used to investigate the correlation structure between the two sets of variables represented by watershed characteristics and flood peaks. Regional frequency analysis is commonly used for the estimation of extreme hydrological or meteorological events, such as floods, at sites where little or no records exist. A homogeneous region is defined as a group of drainage basins whose standardized flood frequency curves are similar, to within a certain margin of sampling error. The Canonical Correlation method also helps to determine the degree of similarity between the groupings of basins in the spaces of the respective canonical variables of the basin and the flood characteristics. It also provides a basis for the determination of homogeneous regions in the space of basin characteristics which takes into account their relationships with the flood characteristics. A data set of 46 stations from Sri Lanka is used to demonstrate the advantage of this method.
dc.description.sponsorship Senate Research Grant en_US
dc.language.iso en en_US
dc.subject Flood-Estimation en_US
dc.subject Research Report en_US
dc.subject Flood Frequency en_US
dc.subject Canonical Correlation en_US
dc.subject Regional Flood en_US
dc.title Regional flood estimation by canonical correlation en_US
dc.type SRC-Report en_US
dc.identifier.department Department of Civil Engineering en_US
dc.identifier.accno 84770 en_US
dc.identifier.year 2004 en_US


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