From forecast skill to economic value: sub-hourly wildfire potential forecasting across Australian regions

Background: Wildfire risk is rising under climate change, yet most operational forecasts rely on daily indices that miss rapid weather shifts preceding ignition. Accurate, timely and economically justified forecasts are critical for early warning and resource allocation. Aims: This study evaluates a sub-hourly machine-learning (ML) forecasting system for fire potential using weather-station data from three Australian regions (Sunshine Coast, Brisbane, Hobart), and quantifies its economic value using a cost–loss framework. Methods: ML classifiers were trained on sub-hourly Automatic Weather Station (AWS) data and benchmarked against the Fire Behaviour Index (FBI). Forecast discrimination was assessed using True Positive Rates (TPRs) and False Positive Rates (FPRs), as well as a Potential Economic Value (PEV) analysis, complemented by learning-curve tests. Key results: The ML model improved forecast skill over the FBI by 10–30%, with the ML system doubling potential savings relative to the FBI. Learning-curve diagnostics indicated that stable performance is achieved after -30 fire events. Conclusions: The ML forecasting framework demonstrates predictive skill and measurable economic value, highlighting its potential for scalable, cost-effective early-warning systems. Implications: By linking forecast skill directly to financial and operational outcomes, this approach provides a quantitative basis for prioritising investment in timely, data-driven fire-warning tools across regions with limited data and constrained budgets.