Articles | Volume 24, issue 12
https://doi.org/10.5194/nhess-24-4385-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-4385-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Dec 2024
Research article |
| 05 Dec 2024
| 05 Dec 2024
Integrating susceptibility maps of multiple hazards and building exposure distribution: a case study of wildfires and floods for the province of Quang Nam, Vietnam
Faculty of Hydraulic Engineering, Hanoi University of Civil Engineering, Hanoi, 100000, Vietnam
Giuseppe Forino
School of Science, Engineering & Environment, University of Salford, Manchester, M5 4WT, UK
Lynda Yorke
School of Environmental and Natural Sciences, Bangor University, Bangor, Gwynedd, LL57 2DG, UK
Department of Geodesy, Hanoi University of Civil Engineering, Hanoi, 100000, Vietnam
Quynh Duy Bui
Department of Geodesy, Hanoi University of Civil Engineering, Hanoi, 100000, Vietnam
Hanh Hong Tran
Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi, 100000, Vietnam
Dinh Quoc Nguyen
Environmental Chemistry and Ecotoxicology Lab, Phenikaa University, Hanoi, 12116, Vietnam
Hieu Cong Duong
Institute of Geodesy Engineering Technology, Hanoi University of Civil Engineering, Hanoi, 100000, Vietnam
Matthieu Kervyn
Department of Geography, Vrije Universiteit Brussel, Brussels, 1050, Belgium
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Risk perception involves the processes of collecting, selecting and interpreting signals about the uncertain impacts of hazards. It may contribute to improving risk communication and motivating the protective behaviour of the population living near volcanoes. Our work describes the spatial variation and factors influencing volcanic risk perception of 2204 adults of Goma exposed to Nyiragongo. It contributes to providing a case study for risk perception understanding in the Global South.
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Short summary
This study presents a novel and integrated approach to assessing the climate hazards of floods and wildfires. We explore multi-hazard assessment and risk through a machine learning modeling approach. The process includes collecting a database of topography, climate, geology, environment, and building data; developing models for multi-hazard assessment and coding in the Google Earth Engine; and producing credible multi-hazard susceptibility and building exposure maps.
This study presents a novel and integrated approach to assessing the climate hazards of floods...
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