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Development of a statistical tool for the estimation of riverbank erosion probability

Varouchakis Emmanouil, Giannakis Georgios, Lilli Maria, Nikolaidis Nikolaos, Karatzas Giorgos, Ioannidou Eleni

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URIhttp://purl.tuc.gr/dl/dias/DC333A15-E009-4C3A-BFE6-003E164C6A85-
Identifierhttps://doi.org/10.5194/soild-2-647-2015-
Identifierhttp://www.soil-discuss.net/2/647/2015/soild-2-647-2015.pdf-
Languageen-
Extent23 pagesen
TitleDevelopment of a statistical tool for the estimation of riverbank erosion probabilityen
CreatorVarouchakis Emmanouilen
CreatorΒαρουχακης Εμμανουηλel
CreatorGiannakis Georgiosen
CreatorΓιαννακης Γεωργιοςel
CreatorLilli Mariaen
CreatorΛιλλη Μαριαel
CreatorNikolaidis Nikolaosen
CreatorΝικολαιδης Νικολαοςel
CreatorKaratzas Giorgosen
CreatorΚαρατζας Γιωργοςel
CreatorIoannidou Elenien
CreatorΙωαννιδου Ελενηel
PublisherCopernicus Publicationsen
DescriptionΔημοσίευση σε επιστημονικό περιοδικόel
Content SummaryRiverbank erosion affects river morphology and local habitat and results in riparian land loss, property and infrastructure damage, and ultimately flood defence weakening. An important issue concerning riverbank erosion is the identification of the vulnerable ar- 5 eas in order to predict river changes and assist stream management/restoration. An approach to predict vulnerable to erosion areas is to quantify the erosion probability by identifying the underlying relations between riverbank erosion and geomorphological or hydrological variables that prevent or stimulate erosion. In the present work, a combined deterministic and statistical methodology is proposed to predict the probability 10 of presence or absence of erosion in a river section. A physically based model determines the vulnerable to erosion locations by quantifying the potential eroded area. The derived results are used to determine validation locations for the statistical tool performance evaluation. The statistical tool is based on a series of independent local variables and employs the Logistic Regression methodology. It is developed in two 15 forms, Logistic Regression and Locally Weighted Logistic Regression, which both deliver useful and accurate results. The second form though provides the most accurate results as it validates the presence or absence of erosion at all validation locations. The proposed methodology is easy to use, accurate and can be applied to any region and river.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-06-
Date of Publication2015-
SubjectRiverbank filtrationen
SubjectRiverbank infiltrationen
SubjectWater--Riverbank filtrationen
Subjectwater purification riverbank filtrationen
Subjectriverbank filtrationen
Subjectriverbank infiltrationen
Subjectwater riverbank filtrationen
SubjectAnalysis, Regressionen
SubjectLinear regressionen
SubjectRegression modelingen
Subjectregression analysisen
Subjectanalysis regressionen
Subjectlinear regressionen
Subjectregression modelingen
Bibliographic CitationE. A. Varouchakis, G. V. Giannakis, M. A. Lilli, E. Ioannidou, N. P. Nikolaidis and G. P. Karatzas, "Development of a statistical tool for the estimation of riverbank erosion probability", SOIL Discuss, vo. 2, pp.647–674, 2015, doi:10.5194/soild-2-647-2015en

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