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

• Published in: Natural hazards and earth system sciences 2024-12, Vol.24 (12), p.4385-4408
• Main Authors: Luu, Chinh, Forino, Giuseppe, Yorke, Lynda, Ha, Hang, Bui, Quynh Duy, Tran, Hanh Hong, Nguyen, Dinh Quoc, Duong, Hieu Cong, Kervyn, Matthieu
• Format: Article
• Language: English
• Subjects: Analysis; Case studies; Climate; Climate and hydrology; Climate change; Climate models; Coastal plains; Coasts; Digital mapping; Dry season; Environmental conditions; Exposure; Fire hazards; Flood relief; Floods; Forest & brush fires; Geology; Hazard assessment; Hazard identification; Hydrology; Land use; Landslides & mudslides; Machine learning; Monsoons; Rain; Rainfall; Regulatory agencies; Risk assessment; River networks; Spatial analysis; Sustainable development; Topography; Tourism; Vegetation; Vietnam; Wildfires; Wind
• ISSN: 1561-8633; 1684-9981
• DOI: 10.5194/nhess-24-4385-2024

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 to multi-hazard (floods and wildfires) exposure estimates using machine learning models, Google Earth Engine, and spatial analysis tools for a typical case study in the province of Quang Nam in Central Vietnam. A geospatial database is built for multiple-hazard modeling, 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 modeled and then integrated into a multi-hazard exposure matrix to demonstrate a hazard profiling approach to multi-hazard risk assessment. The results are explicitly illustrated for flood and wildfire hazards and the exposure of buildings. Susceptibility models using the random forest approach provide model accuracy of AUC (area under the receiver operating characteristic curve) = 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.