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Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential

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dc.citedby30
dc.contributor.authorCostache R.en_US
dc.contributor.authorPham Q.B.en_US
dc.contributor.authorCorodescu-Ro?ca E.en_US
dc.contributor.authorC�mpianu C.en_US
dc.contributor.authorHong H.en_US
dc.contributor.authorThuy Linh N.T.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorVojtek M.en_US
dc.contributor.authorPandhiani S.M.en_US
dc.contributor.authorMinea G.en_US
dc.contributor.authorCiobotaru N.en_US
dc.contributor.authorPopa M.C.en_US
dc.contributor.authorDiaconu D.C.en_US
dc.contributor.authorPham B.T.en_US
dc.contributor.authorid55888132500en_US
dc.contributor.authorid57208495034en_US
dc.contributor.authorid57216950263en_US
dc.contributor.authorid57200754374en_US
dc.contributor.authorid55630331400en_US
dc.contributor.authorid57211268069en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56044858400en_US
dc.contributor.authorid56770084200en_US
dc.contributor.authorid56001567800en_US
dc.contributor.authorid57194241855en_US
dc.contributor.authorid57209616439en_US
dc.contributor.authorid57189031449en_US
dc.contributor.authorid57818304300en_US
dc.date.accessioned2023-05-29T08:10:14Z
dc.date.available2023-05-29T08:10:14Z
dc.date.issued2020
dc.descriptionCatchments; Climate change; Earth (planet); Floods; Image processing; Land use; Machine learning; Multilayer neural networks; Remote sensing; Runoff; Geographically weighted regression; Land use/land cover change; Machine learning techniques; Meteorological phenomena; Multilayer perceptron neural networks; Pearson coefficient; Runoff potentials; Synthetic dynamics; Geographic information systemsen_US
dc.description.abstractThe aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zabala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km2. The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zabala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1422
dc.identifier.doi10.3390/RS12091422
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85085504419
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085504419&doi=10.3390%2fRS12091422&partnerID=40&md5=998ff6628f56ed2d1b8d105ea9480516
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25502
dc.identifier.volume12
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleRemote Sensing
dc.titleUsing GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potentialen_US
dc.typeArticleen_US
dspace.entity.typePublication
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