A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment

Nanda Khoirunisa, Cheng-Yu Ku and Chih-Yu Liu
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Nanda Khoirunisa: Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan
Cheng-Yu Ku: Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan
Chih-Yu Liu: Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan

IJERPH, 2021, vol. 18, issue 3, 1-20

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 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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