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A Multiple linear regression GIS module using spatial variables to model orographic rainfall

Tsanis Giannis, S. Naoum

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URIhttp://purl.tuc.gr/dl/dias/19272AEE-A0D7-460D-A48F-773A7F8E3D8F-
Languageen-
Extent15 pagesen
TitleA Multiple linear regression GIS module using spatial variables to model orographic rainfallen
CreatorTsanis Giannisen
CreatorΤσανης Γιαννηςel
CreatorS. Naoumen
Content SummaryThis paper aims to document the development of a new GIS-based spatial interpolation module that adopts a multiple linear regression technique. The functionality of the GIS module is illustrated through a test case represented by the island of Crete, Greece, where the models generated were applied to locations where estimates of annual precipitation were required. The response variable is ‘precipitation’ and the predictor variables are ‘elevation’, ‘longitude’ and ‘latitude’, or any combination of these. The module is capable of performing a sequence of tasks which will eventually lead to an estimation of mean areal precipitation and the total volume of precipitation. In addition, it can generate up to nine predictor variables and their parameters, and can estimate areal rainfall for a user-specified three-dimensional extent. The developed module performed satisfactorily. Precipitation estimates at ungauged locations were obtained using the multiple linear regression method in addition to some conventional spatial interpolation techniques (i.e. IDW, Spline, Kriging, etc.). The multiple linear regression models provided better estimates than the other spatial interpolation techniques. en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-09-
Date of Publication2004-
Subject--Cases, clinical reports, statisticsen
Subject--Statistical dataen
Subjectstatisticsen
Subjectcases clinical reports statisticsen
Subjectstatistical dataen
Bibliographic CitationS. Naoum, I.K Tsanis, “A Multiple linear regression GIS module using spatial variables to model orographic rainfall”, J. of Hydroinf., vol. 6, pp. 39-54, 2004.en

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