Machine learning prediction of fine woody fuel consumption in surface fires burning in eucalypt forest fuels

Background Accurate prediction of the woody debris consumed in wildfires is important for both wildland management and carbon accounting. Aims We investigate the combustion factor (defined as the mean diameter reduction rate of the assumed cylindrical woody debris after fire) for fine woody debris (FWD) with pre-burn diameters ranging from 6 to 50 mm in free-spreading surface fires. Methods Experiments were conducted in the CSIRO Pyrotron combustion wind tunnel facility (Canberra, Australia). A database of FWD consumption was constructed from experimental observations featuring 17 predictor variables. Machine learning models were applied to predict the FWD combustion factor. Key results Pearson correlation coefficient analysis indicated that the FWD combustion factor exhibited highly significant negative correlations with smouldering duration, pre-burn diameter and tunnel axial position of FWD. Conclusions We conclude that our combustion wind tunnel experimental approach captures the underpinning fire behaviour physics of FWD consumption well. A binary classification model using a support vector classifier demonstrated the best results for predicting FWD consumption, with an overall classification accuracy of 74%. A ridge regression model achieved a mean absolute error of approximately 9% for modelling FWD consumption. Implications Our results illuminate possible options for controlling woody fuel consumption during managed fires in landscapes.