  Fire has been an integral part of the Earth for millennia. Several recent
wildfires have exhibited an unprecedented spatial and temporal extent and their
control is beyond national firefighting capabilities. Prescribed or controlled
burning treatments are debated as a potential measure for ameliorating the
spread and intensity of wildfires. Machine learning analysis using random
forests was performed in a spatio-temporal data set comprising a large number
of savanna fires across 22 years. Results indicate that fire return interval
was not an important predictor of fire spread rate or fire intensity, having a
feature importance of 3.5%, among eight other predictor variables. Manipulating
burn seasonality showed a feature importance of 6% or less regarding fire
spread rate or fire intensity. While manipulated fire return interval and
seasonality moderated both fire spread rate and intensity, their overall
effects were low in comparison with meteorological (hydrological and climatic)
variables. The variables with the highest feature importance regarding fire
spread rate resulted in fuel moisture with 21%, relative humidity with 15%,
wind speed with 14%, and last years rainfall with 14%. The variables with the
highest feature importance regarding fire intensity included fuel load with
21.5%, fuel moisture with 16.5%, relative humidity with 12.5%, air temperature
with 12.5%, and rainfall with 12.5%. Predicting fire spread rate and intensity
has been a poor endeavour thus far and we show that more data of the variables
already monitored would not result in higher predictive accuracy.
