Looking into the green roof scenario to mitigate flash flood effects in Mamak, Turkey, via classifying images of Sentinel-1, 2, and PlanetScope satellites with LibSVM algorithm in Google Earth Engine cloud platform

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dc.authoridAghlmand, Majid/0000-0003-0534-5393
dc.authoridPouya, Sima/0000-0001-6419-1756
dc.authoridKarsli, Fevzi/0000-0002-0411-3315
dc.authorwosidAghlmand, Majid/ACO-2322-2022
dc.authorwosidPouya, Sima/AAA-6397-2021
dc.authorwosidKarsli, Fevzi/AAT-3587-2020
dc.contributor.authorPouya, Sima
dc.contributor.authorAghlmand, Majid
dc.contributor.authorKarsli, Fevzi
dc.date.accessioned2024-08-04T20:53:04Z
dc.date.available2024-08-04T20:53:04Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis research aimed to increase the green space factor to mitigate flash flood effects on urban storm water runoff in the Ankara Mamak region and to minimize the damages by flash floods. The land use/cover map was first obtained by using the images of Sentinel-1, Sentinel-2, and PlanetScope satellites with the LIBSVM algorithm on the Google Earth Engine. The GSF value was then calculated and it was low (0.26) compared to world standards. This study was proposed as a solution for the flood disaster, using the extensive green roof scenario. After green roof conversion scenarios, the GSF value was recalculated. It was found to be above the minimum of green infrastructure that human settlements should achieve, regardless of density or land use (0.43). Offering high resolution images and the possibility of processing them via different algorithms of machine learning has revolutionized the environmental and urban-related studies as they help urban managers and planners to make decisions accurately and quickly.en_US
dc.identifier.doi10.37040/geografie.2022.008
dc.identifier.endpage240en_US
dc.identifier.issn1212-0014
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85140001359en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage219en_US
dc.identifier.urihttps://doi.org/10.37040/geografie.2022.008
dc.identifier.urihttps://hdl.handle.net/11616/100946
dc.identifier.volume127en_US
dc.identifier.wosWOS:000887433400002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCzech Geographic Socen_US
dc.relation.ispartofGeografieen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGoogle Earth Engineen_US
dc.subjectSentinel 1en_US
dc.subjectSentinel 2en_US
dc.subjectPlanetScopeen_US
dc.subjectGreen spaces factoren_US
dc.subjectflash floodsen_US
dc.subjectgreen roofsen_US
dc.subjectAnkara/Mamak districten_US
dc.titleLooking into the green roof scenario to mitigate flash flood effects in Mamak, Turkey, via classifying images of Sentinel-1, 2, and PlanetScope satellites with LibSVM algorithm in Google Earth Engine cloud platformen_US
dc.typeArticleen_US

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