Geoinformatics and Machine Learning for Comprehensive Fire Risk Assessment and Management in Peri-Urban Environments: A Building-Block-Level Approach

• Published in: Applied sciences 2023-09, Vol.13 (18), p.10261
• Main Authors: Yfantidou, Anastasia, Zoka, Melpomeni, Stathopoulos, Nikolaos, Kokkalidou, Martha, Girtsou, Stella, Tsoutsos, Michail-Christos, Hadjimitsis, Diofantos, Kontoes, Charalampos
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Biodiversity; Climate change; Computer software industry; Data analysis; Decision making; Decision support systems; Disasters; Droughts; Ecosystems; Emergency preparedness; Environmental economics; Evacuations & rescues; field observations; fire hazard simulations; Fire protection; fire risk assessment; fire risk prediction; fire spread; Forest & brush fires; Forest fires; Geographic information systems; Geospatial data; Global temperature changes; Greece; Integrated approach; Machine learning; management planning; Methods; Precipitation (Meteorology); Remote sensing; Risk assessment; Role models; Trends; United States; Urban areas
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app131810261

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m2) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas.