Publication:
Obtaining the Manning Roughness With Terrestrial-Remote Sensing Technique and Flood Modeling Using FLO-2D: A Case Study Samsun From Turkey

dc.authorscopusid57147393600
dc.authorscopusid33568443200
dc.authorwosidDemir, Vahdettin/Aad-3764-2020
dc.authorwosidDemir, Vahdettin/Aad-3764-2020
dc.contributor.authorDemir, Vahdettin
dc.contributor.authorKeskin, Asli Ulke
dc.contributor.authorIDDemir, Vahdettin/0000-0002-6590-5658
dc.date.accessioned2025-12-11T00:54:21Z
dc.date.issued2020
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Demir, Vahdettin] KTO Karatay Univ, Dept Civil Engn, Fac Engn, Konya, Turkey; [Keskin, Asli Ulke] Ondokuz Mayis Univ, Fac Engn, Dept Civil Engn, Samsun, Turkeyen_US
dc.descriptionDemir, Vahdettin/0000-0002-6590-5658;en_US
dc.description.abstractDetermining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio kappa was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.en_US
dc.description.woscitationindexScience Citation Index Expanded
dc.identifier.doi10.15233/gfz.2020.37.9
dc.identifier.endpage156en_US
dc.identifier.issn0352-3659
dc.identifier.issn1846-6346
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85098702571
dc.identifier.scopusqualityQ3
dc.identifier.startpage131en_US
dc.identifier.urihttps://doi.org/10.15233/gfz.2020.37.9
dc.identifier.urihttps://hdl.handle.net/20.500.12712/40138
dc.identifier.volume37en_US
dc.identifier.wosWOS:000608071700002
dc.identifier.wosqualityQ3
dc.language.isoenen_US
dc.publisherUniv Zagreb, Andrija Mohorovicic Geophys Insten_US
dc.relation.ispartofGeofizikaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectManningen_US
dc.subjectCowan Methoden_US
dc.subjectClassificationen_US
dc.subjectFlood Propagation Mapen_US
dc.subjectFLO-2Den_US
dc.subjectHEC-RASen_US
dc.titleObtaining the Manning Roughness With Terrestrial-Remote Sensing Technique and Flood Modeling Using FLO-2D: A Case Study Samsun From Turkeyen_US
dc.typeArticleen_US
dspace.entity.typePublication

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