TY - JOUR
T1 - Using GIS, remote sensing, and machine learning to highlight the correlation between the land-use/land-cover changes and flash-flood potential
AU - Costache, Romulus
AU - Pham, Quoc Bao
AU - Corodescu-Roşca, Ema
AU - Cîmpianu, Cătălin
AU - Hong, Haoyuan
AU - Thuy Linh, Nguyen Thi
AU - Fai, Chow Ming
AU - Ahmed, Ali Najah
AU - Vojtek, Matej
AU - Pandhiani, Siraj Muhammed
AU - Minea, Gabriel
AU - Ciobotaru, Nicu
AU - Popa, Mihnea Cristian
AU - Diaconu, Daniel Constantin
AU - Pham, Binh Thai
N1 - Publisher Copyright:
© 2020 by the authors.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Flash-flood potential index
KW - Geographically weighted regression
KW - Landsat images
KW - Multilayer perceptron
KW - Total relative difference-synthetic dynamic land-use index
KW - Zăbala
UR - http://www.scopus.com/inward/record.url?scp=85085504419&partnerID=8YFLogxK
U2 - 10.3390/RS12091422
DO - 10.3390/RS12091422
M3 - Article
AN - SCOPUS:85085504419
SN - 2072-4292
VL - 12
JO - Remote Sensing
JF - Remote Sensing
IS - 9
M1 - 1422
ER -
