Local drivers of anthropogenic forest fire ignition vulnerability in Austria

Abstract Background Forest fires are becoming more frequent and destructive under climate change, threatening ecosystems, and society. Existing machine learning approaches for modeling forest fire ignition vulnerability often lack the spatial resolution needed for local-scale estimation, rely on limited proxies for anthropogenic ignition pressure, and provide only limited insight into how ignition drivers and their importance vary across local conditions, which limits their usefulness for developing locally meaningful fire danger models. Results We developed a machine learning framework for anthropogenic forest fire ignition vulnerability in Austria using 1569 documented fire events from 2012 to 2021 and an equal number of generated non-fire samples. The model integrates high-resolution environmental, socioeconomic, infrastructure-related, and human movement variables to estimate probabilistic vulnerability scores at spatially resolved point locations across Austria. A web based tool visualizes these predictions on a 50 by 50 m grid with adaptive aggregation at lower zoom levels. CatBoost and Random Forest achieved a precision of 80.7% and an accuracy of 77.9%, exceeding earlier Austrian studies with accuracies of around 60%. Both models produced highly consistent spatial patterns and identified fire prone areas especially in eastern and southern Austria. Explainable AI analyses showed positive effects of Running Frequency, Building Density, and Fine Fuel Moisture Code, while slope showed a negative effect, thereby making local ignition drivers and their spatial relevance transparent. Conclusions The proposed framework enables high-resolution mapping of anthropogenic forest fire ignition vulnerability and Explainable Artificial Intelligence (XAI)-driven multi-criteria evaluation, while revealing the relative influence of environmental and anthropogenic drivers. The introduced models identify historically fire-prone zones in Austria at both local and regional scales and indicate that anthropogenic factors, together with established environmental conditions, are key drivers of ignition vulnerability. More broadly, the methodological approach and findings illustrate how interpretable and explainable machine learning can help assess which variables are relevant under different local conditions, thereby supporting targeted mitigation and adaptation planning as well as more context-aware model development. Further work is needed to evaluate ignition danger under changing anthropogenic and environmental pressures.