TY - JOUR

T1 - Integrating susceptibility maps of multiple hazards and building exposure distribution: A case study of wildfires and floods for Quang Nam province, Vietnam

AU - Luu, Chinh

AU - Forino, Giuseppe

AU - Yorke, Lynda

AU - Ha, Hang

AU - Bui, Quynh Duy

AU - Tran, Hanh Hong

AU - Nguyen, Dinh Quoc

AU - Duong, Hieu Cong

AU - Kervyn, Matthieu

PY - 2024/10/7

Y1 - 2024/10/7

N2 - Natural hazards have serious impacts worldwide on society, economy and environment. In Vietnam, throughout the years, natural hazards have caused significant loss of lives as well as severe devastation to houses, crops, and transportation. This research presents a new approach for multi-hazard (floods and wildfires) exposure estimates using machine learning models, Google Earth Engine, and spatial analysis tools for a typical case study, Quang Nam province in Central Vietnam. A geospatial database is built for multiple hazard modelling, including an inventory of climate-related hazards (floods and wildfires), topography, geology, hydrology, climate features (temperature, rainfall, wind), land use, and building data for exposure assessment. The susceptibility of each hazard is first modelled and then integrated into a multi-hazard exposure matrix to demonstrate a hazard profiling approach for multi-hazard risk assessment. 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 AUC = 0.882 and 0.884 for floods and wildfires, respectively. The flood and wildfire hazards are combined within a semi-quantitative matrix to assess the building exposure to different hazards. Digital multi-hazard exposure maps of floods and wildfires aid the identification of areas exposed to climate-related hazards and the potential impacts of hazards. This approach can be used to inform communities and regulatory authorities on where to develop and implement long-term adaptation solutions.

AB - Natural hazards have serious impacts worldwide on society, economy and environment. In Vietnam, throughout the years, natural hazards have caused significant loss of lives as well as severe devastation to houses, crops, and transportation. This research presents a new approach for multi-hazard (floods and wildfires) exposure estimates using machine learning models, Google Earth Engine, and spatial analysis tools for a typical case study, Quang Nam province in Central Vietnam. A geospatial database is built for multiple hazard modelling, including an inventory of climate-related hazards (floods and wildfires), topography, geology, hydrology, climate features (temperature, rainfall, wind), land use, and building data for exposure assessment. The susceptibility of each hazard is first modelled and then integrated into a multi-hazard exposure matrix to demonstrate a hazard profiling approach for multi-hazard risk assessment. 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 AUC = 0.882 and 0.884 for floods and wildfires, respectively. The flood and wildfire hazards are combined within a semi-quantitative matrix to assess the building exposure to different hazards. Digital multi-hazard exposure maps of floods and wildfires aid the identification of areas exposed to climate-related hazards and the potential impacts of hazards. This approach can be used to inform communities and regulatory authorities on where to develop and implement long-term adaptation solutions.

M3 - Article

JO - Natural Hazards and Earth System Sciences

JF - Natural Hazards and Earth System Sciences

ER -