Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment

Demissie, Zelalem; Rimal, Prashant; Seyoum, Wondwosen M.; Dutta, Atri; Rimmington, Glen

doi:10.1016/j.acags.2024.100183

Applied Computing and Geosciences

2024

DOI: 10.1016/j.acags.2024.100183

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Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment

Zelalem Demissie,

Prashant Rimal,

Wondwosen M. Seyoum

et al.

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2024

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Cited by 3 publications

References 47 publications

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Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Debnath,

Sahariah,

Meraj

et al. 2024

Physics and Chemistry of the Earth, Parts A/B/C

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Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Debnath,

Sahariah,

Meraj

et al. 2024

Physics and Chemistry of the Earth, Parts A/B/C

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Urban flood resilience evaluation in China: a systematic review of frameworks, methods, and limitations

Long,

Junjia,

Jiao

et al. 2024

Geomatics, Natural Hazards and Risk

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Spatio-Temporal Analysis of Susceptibility Hazardous and Risk Mapping in Post-2023 Simangulampe Devastating Flood

Tampubolon,

Yanti,

Hutahaean

et al. 2024

J. Phys.: Conf. Ser.

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Over 169 people along the Simangulampe upstream were under devastating flood and worst landslide watches in December 2023 due to a significant storm bringing the heaviest rainfall and moving giant boulders. Indeed, there are far fewer studies and information on susceptibility hazards in Simangalumpe than in others. First-rate information on impressive risk mitigation strategies increased climate-change consideration and reduced flood risk. We adopt C-band synthetic aperture radar and multispectral imagery from Sentinel to identify, visualize, and analyze flash flood mapping and mitigating to address this issue. Precisely, Simangalumpe flood is considered from surface water indices with various parameters: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and SAR flood inundation mapping. Results show low NDVI values- over 50 percent of plant canopies are damaged (uprooted and broken trees) along Simangulampe upstream. Combining multispectral and SAR properties of the surface water index shows the surface extent of water bodies in Simagalumpe upstream and covers giant boulders. Finally, developing spatial and temporal analysis from multispectral and SAR data results in information on mitigating flash flooding and reducing unnecessary threats.

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Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment

Cited by 3 publications

References 47 publications

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Urban flood resilience evaluation in China: a systematic review of frameworks, methods, and limitations

Spatio-Temporal Analysis of Susceptibility Hazardous and Risk Mapping in Post-2023 Simangulampe Devastating Flood

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