Preprints
https://doi.org/10.5194/egusphere-2024-57
https://doi.org/10.5194/egusphere-2024-57
29 Jan 2024
 | 29 Jan 2024
Status: this preprint is open for discussion.

Integrating multi-hazard susceptibility and building exposure: A case study for Quang Nam province, Vietnam

Chinh Luu, Giuseppe Forino, Lynda Yorke, Hang Ha, Quynh Duy Bui, Hanh Hong Tran, Dinh Quoc Nguyen, Hieu Cong Duong, and Matthieu Kervyn

Abstract. Natural hazards have serious impacts worldwide on society, economy and environment. In Vietnam, throughout the years, natural hazards have caused a significant loss of lives as well as severe devastation to houses, crops, and transportation. This paper presents a new model for multi-hazard (floods and wildfires) exposure estimates using machine learning models, Google Earth Engine, and spatial analysis tools for a typical Quang Nam province, Vietnam case study. By establishing the context and collected data on climate hazards and impacts, a geospatial database was built for multiple hazard modelling, including an inventory of climate-related hazards (floods and wildfires), topography, geology, hydrology, climate features (temperature, wetness, wind), land use, and building data for exposure assessment. The hazard susceptibility and exposure matrices were presented to demonstrate a hazard profiling approach for multi-hazards. The results are explicitly illustrated for floods and wildfire hazards and the exposure of buildings. Susceptibility models using the random forest approach provide model accuracy of the AUC=0.882 and 0.884 for floods and wildfires, respectively. The flood and wildfire hazards are combined within a semi-quantitative matrix for assessing the building exposure to different combinations of hazards. Digital multi-hazard risk and exposure maps of floods and wildfires aid the identification of areas prone to climate-related hazards and the potential impacts of hazards. This approach can be used to inform communities and regulatory authorities on how they develop and implement long-term adaptation solutions.

Chinh Luu, Giuseppe Forino, Lynda Yorke, Hang Ha, Quynh Duy Bui, Hanh Hong Tran, Dinh Quoc Nguyen, Hieu Cong Duong, and Matthieu Kervyn

Status: open (until 13 Mar 2024)

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  • RC1: 'Comment on egusphere-2024-57', Julius Schlumberger, 18 Feb 2024 reply
Chinh Luu, Giuseppe Forino, Lynda Yorke, Hang Ha, Quynh Duy Bui, Hanh Hong Tran, Dinh Quoc Nguyen, Hieu Cong Duong, and Matthieu Kervyn
Chinh Luu, Giuseppe Forino, Lynda Yorke, Hang Ha, Quynh Duy Bui, Hanh Hong Tran, Dinh Quoc Nguyen, Hieu Cong Duong, and Matthieu Kervyn

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Short summary
The study produced a novel and integrated approach to assessing the climate hazards of floods and wildfires. We explored multi-hazards assessment and risk through a machine learning modelling approach. The process includes (1) collecting a database of topography, climate, geology, environment, and building data, (2) developing models for multi-hazards assessment and coding in Google Earth Engine, and (3) producing credible multi-hazard susceptibility and building exposure maps.