Abstract: 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.
Keywords: geographic information system; back-propagation neural network; rainfall; historical
flood; prediction
https://doi.org/10.3390/ijerph18031072
https://www.mdpi.com/1660-4601/18/3/1072/pdf
