Flood Susceptibility Mapping Using a Frequency Ratio Model: A Case Study of Chai Nat Province, Thailand

Duangyiwa, Chanita; Cheewinsiriwat, Pannee

doi:10.1007/978-3-031-16217-6_1

Cited by 1 publication

(2 citation statements)

References 24 publications

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“…The magnitude of a flash flood is the most important factor in determining its impact. Flood-related direct or indirect damages can be divided into materialistic and non-materialistic effects when calculating overall effects [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Climate change has become one of the most serious threats facing the planet. Global warming and the consequent problems of drought, desertification, and floods is perhaps the most prominent form of that change, which affects all continents of the world [2][3][4][5][6]. Even the Arab region, the heart of the world, has become the epicenter of climate change.…”

Section: Introductionmentioning

confidence: 99%

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Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt

Megahed,

Abdo,

AbdelRahman

et al. 2023

Applied Sciences

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The occurrence of flash floods is a natural yet unavoidable occurrence over time. In addition to harming people, property, and resources, it also undermines a country’s economy. This paper attempts to identify areas of flood vulnerability using a frequency ratio approach. The frequency ratio (FR) model was used to produce flood prediction maps for New Cairo City, Egypt. Using field data and remote sensing data, 143 spatial flooded point sites were mapped to build a flood inventory map. The primary driving criteria for flash floods were determined to be elevation, slope, aspect, Land Use Land Cover (LULC), lithology, stream distance, stream density, topographic wetness index (TWI), surface runoff, and terrain ruggedness index (TRI), in that order of importance. A flood susceptibility map (FSM) has been created using the FR model, which combines geographical flooded sites and environmental variables. Our findings from FSM, roughly a fifth of the city is very highly susceptible to flooding (19.32%), while the remaining 40.09% and 13.14% of the study area rank very low and low risk, respectively. The receiver operating characteristic curve (ROC) technique was also used to validate the FSM, and the resulting results showed an area under the curve (AUC) of 90.11%. In conclusion, decision makers can employ models to extract and generate flood risk maps in order to better understand the effects of flash floods and to create alternative measures to prevent this hazard in similar regions. The results of this study will aid planners and decision makers in developing some likely actions to reduce floods vulnerability in this area.

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