A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment
Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan
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Authors to whom correspondence should be addressed.
Academic Editors: M. Dolores Esteban, José-Santos López-Gutiérrez, Vicente Negro and José Marcos Ortega
Int. J. Environ. Res. Public Health 2021, 18(3), 1072; https://doi.org/10.3390/ijerph18031072
Received: 12 December 2020
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Revised: 21 January 2021
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Accepted: 21 January 2021
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Published: 26 January 2021
(This article belongs to the Special Issue Environmental Sustainability in Maritime Infrastructures)
This article presents a geographic information system (GIS)-based artificial neural network (GANN) model for flood susceptibility assessment of Keelung City, Taiwan. Various factors, including elevation, slope angle, slope aspect, flow accumulation, flow direction, topographic wetness index (TWI), drainage density, rainfall, and normalized difference vegetation index, were generated using a digital elevation model and LANDSAT 8 imagery. Historical flood data from 2015 to 2019, including 307 flood events, were adopted for a comparison of flood susceptibility. Using these factors, the GANN model, based on the back-propagation neural network (BPNN), was employed to provide flood susceptibility. The validation results indicate that a satisfactory result, with a correlation coefficient of 0.814, was obtained. A comparison of the GANN model with those from the SOBEK model was conducted. The comparative results demonstrated that the proposed method can provide good accuracy in predicting flood susceptibility. The results of flood susceptibility are categorized into five classes: Very low, low, moderate, high, and very high, with coverage areas of 60.5%, 27.4%, 8.6%, 2.5%, and 1%, respectively. The results demonstrate that nearly 3.5% of the study area, including the core district of the city and an exceedingly populated area including the financial center of the city, can be categorized as high to very high flood susceptibility zones.
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Keywords:
geographic information system; back-propagation neural network; rainfall; historical flood; prediction
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MDPI and ACS Style
Khoirunisa, N.; Ku, C.-Y.; Liu, C.-Y. A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment. Int. J. Environ. Res. Public Health 2021, 18, 1072. https://doi.org/10.3390/ijerph18031072
AMA Style
Khoirunisa N, Ku C-Y, Liu C-Y. A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment. International Journal of Environmental Research and Public Health. 2021; 18(3):1072. https://doi.org/10.3390/ijerph18031072
Chicago/Turabian StyleKhoirunisa, Nanda, Cheng-Yu Ku, and Chih-Yu Liu. 2021. "A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment" International Journal of Environmental Research and Public Health 18, no. 3: 1072. https://doi.org/10.3390/ijerph18031072
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