Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential
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 Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial pat...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/9/1422 |
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| author | Romulus Costache Quoc Bao Pham Ema Corodescu-Roșca Cătălin Cîmpianu Haoyuan Hong Nguyen Thi Thuy Linh Chow Ming Fai Ali Najah Ahmed Matej Vojtek Siraj Muhammed Pandhiani Gabriel Minea Nicu Ciobotaru Mihnea Cristian Popa Daniel Constantin Diaconu Binh Thai Pham |
| author_facet | Romulus Costache Quoc Bao Pham Ema Corodescu-Roșca Cătălin Cîmpianu Haoyuan Hong Nguyen Thi Thuy Linh Chow Ming Fai Ali Najah Ahmed Matej Vojtek Siraj Muhammed Pandhiani Gabriel Minea Nicu Ciobotaru Mihnea Cristian Popa Daniel Constantin Diaconu Binh Thai Pham |
| author_sort | Romulus Costache |
| collection | DOAJ |
| description | 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 Zăbala 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 km<sup>2</sup>. 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 Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential. |
| first_indexed | 2024-03-10T20:07:56Z |
| format | Article |
| id | doaj.art-e433f0e9d90542568bb8680338921bd0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:07:56Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e433f0e9d90542568bb8680338921bd02023-11-19T23:10:22ZengMDPI AGRemote Sensing2072-42922020-04-01129142210.3390/rs12091422Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood PotentialRomulus Costache0Quoc Bao Pham1Ema Corodescu-Roșca2Cătălin Cîmpianu3Haoyuan Hong4Nguyen Thi Thuy Linh5Chow Ming Fai6Ali Najah Ahmed7Matej Vojtek8Siraj Muhammed Pandhiani9Gabriel Minea10Nicu Ciobotaru11Mihnea Cristian Popa12Daniel Constantin Diaconu13Binh Thai Pham14Research Institute of the University of Bucharest, Bucharest, 90-92 Sos. Panduri, 5th District, 050663 Bucharest, RomaniaEnvironmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City 70000, VietnamDepartment of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, Iaşi 700505, RomaniaDepartment of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, Iaşi 700505, RomaniaKey Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, ChinaFaculty of Water Resource Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi 100000, VietnamInstitute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor 43000, MalaysiaInstitute of Energy Infrastructure (IEI), Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, MalaysiaDepartment of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, 94974 Nitra, SlovakiaDepartment of General Studies, Jubail University College, Royal Commission of Jubail, Jubail 31961, Saudi ArabiaNational Institute of Hydrology and Water Management, 97E Sos. Bucuresti-Ploiesti, 1st District, 013686 Bucharest, RomaniaNational Institute of Hydrology and Water Management, 97E Sos. Bucuresti-Ploiesti, 1st District, 013686 Bucharest, RomaniaSimion Mehedinți—Nature and Sustainable Development” Doctoral School, University of Bucharest, Bucharest 010041, RomaniaCenter for Integrated Analysis and Territorial Management, University of Bucharest, 010041 Bucharest, RomaniaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamThe aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala 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 km<sup>2</sup>. 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 Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.https://www.mdpi.com/2072-4292/12/9/1422ZăbalaLandsat imagesmultilayer perceptrontotal relative difference-synthetic dynamic land-use indexflash-flood potential indexgeographically weighted regression |
| spellingShingle | Romulus Costache Quoc Bao Pham Ema Corodescu-Roșca Cătălin Cîmpianu Haoyuan Hong Nguyen Thi Thuy Linh Chow Ming Fai Ali Najah Ahmed Matej Vojtek Siraj Muhammed Pandhiani Gabriel Minea Nicu Ciobotaru Mihnea Cristian Popa Daniel Constantin Diaconu Binh Thai Pham Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential Remote Sensing Zăbala Landsat images multilayer perceptron total relative difference-synthetic dynamic land-use index flash-flood potential index geographically weighted regression |
| title | 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_short | 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 |
| topic | Zăbala Landsat images multilayer perceptron total relative difference-synthetic dynamic land-use index flash-flood potential index geographically weighted regression |
| url | https://www.mdpi.com/2072-4292/12/9/1422 |
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