Fire is an integral part of the Earth for millennia. Recent wildfires exhibited an unprecedented spatial and temporal extend 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 controlled fire return interval accounts of 3.5\% of fire spread and 3.5\% of fire intensity. Manipulating burn seasonality accounted for 5\% of fire spread and 6\% of fire intensity. While manipulated fire return interval and seasonality moderated both fire spread  and intensity, their overall effects were low in comparison with  hydrological and climatic  variables. Predicting fire spread  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. Given that the main driving factors of fire spread are related to hydrological and climatic variables, we suggest investigating further the use of climatic refugia against wildfires
Fire is 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 controlled fire return interval exhibited a feature importance of 3.5\% regarding fire spread rate and 3.5\% regarding fire intensity. Manipulating burn seasonality showed a feature importance of 5\% regarding fire spread rate and 6\% regarding 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. 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. 
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Recent wildfires
Several recent wildfires have
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exhibited a feature importance
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of fire spread
regarding fire spread rate
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accounted for
showed a feature importance of
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of fire spread
regarding fire spread rate
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rate
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meteorological (
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Given that the main driving factors of fire spread are related to hydrological and climatic variables, we suggest investigating further the use of climatic refugia against wildfires