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.
翻译:数千年来,火灾一直是地球不可分割的一部分。 最近的几起野火已经展现出前所未有的空间和时间范围,而且其控制超出了国家消防能力。 对限制或控制燃烧处理作为改善野火扩散和强度的潜在措施,可以用来改善野火扩散和强度。 使用随机森林的机器学习分析是在一个由22年大量热带草原火灾组成的时空数据集中进行的。 结果表明,火灾返回间隔不是火率或火烈度的重要预测,在另外8个预测或变数中,火灾蔓延速度或火烈度的显著重要性为3.5%。 火烧季节性调整显示火灾蔓延速度或火烈度的特性重要性为6%或更低。 被操纵的火灾返回间隔和季节性反应减缓了火灾蔓延速度和强度的幅度。 与气象(水利和气候)变量相比,使用随机森林的学习分析效果较低。 火灾蔓延速度具有最高特点的变量导致燃料湿度为21%,相对湿度为15%,风速为14%,降雨量为14 %。 火灾强度最高的变数中,火灾强度最高的变量包括燃料密度为21.5%的准确度,而温度为12.5 %,燃料温度则显示,燃料温度为1.5%,燃料温度为16.5 %,温度为1.5%,而温度为16.5 温度为甚高的数据显示为1.5%,温度为1.5%,温度为1.5%,温度为16.5