Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential
Catchments; 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 ne...
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my.uniten.dspace-255022023-05-29T16:10:14Z Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential Costache R. Pham Q.B. Corodescu-Ro?ca E. C�mpianu C. Hong H. Thuy Linh N.T. Fai C.M. Ahmed A.N. Vojtek M. Pandhiani S.M. Minea G. Ciobotaru N. Popa M.C. Diaconu D.C. Pham B.T. 55888132500 57208495034 57216950263 57200754374 55630331400 57211268069 57214146115 57214837520 56044858400 56770084200 56001567800 57194241855 57209616439 57189031449 57818304300 Catchments; 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 systems The 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. Final 2023-05-29T08:10:14Z 2023-05-29T08:10:14Z 2020 Article 10.3390/RS12091422 2-s2.0-85085504419 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085504419&doi=10.3390%2fRS12091422&partnerID=40&md5=998ff6628f56ed2d1b8d105ea9480516 https://irepository.uniten.edu.my/handle/123456789/25502 12 9 1422 All Open Access, Gold, Green MDPI AG Scopus |
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Catchments; 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 systems |
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55888132500 |
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55888132500 Costache R. Pham Q.B. Corodescu-Ro?ca E. C�mpianu C. Hong H. Thuy Linh N.T. Fai C.M. Ahmed A.N. Vojtek M. Pandhiani S.M. Minea G. Ciobotaru N. Popa M.C. Diaconu D.C. Pham B.T. |
| format |
Article |
| author |
Costache R. Pham Q.B. Corodescu-Ro?ca E. C�mpianu C. Hong H. Thuy Linh N.T. Fai C.M. Ahmed A.N. Vojtek M. Pandhiani S.M. Minea G. Ciobotaru N. Popa M.C. Diaconu D.C. Pham B.T. |
| spellingShingle |
Costache R. Pham Q.B. Corodescu-Ro?ca E. C�mpianu C. Hong H. Thuy Linh N.T. Fai C.M. Ahmed A.N. Vojtek M. Pandhiani S.M. Minea G. Ciobotaru N. Popa M.C. Diaconu D.C. Pham B.T. Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential |
| author_sort |
Costache R. |
| title |
Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential |
| title_short |
Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential |
| title_full |
Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential |
| title_fullStr |
Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential |
| title_full_unstemmed |
Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential |
| title_sort |
using gis, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806427546262175744 |