Using machine learning to predict fire‐ignition occurrences from lightning forecasts,Meteorological Applications

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Using machine learning to predict fire‐ignition occurrences from lightning forecasts
Meteorological Applications ( IF 2.3 ) Pub Date : 2021-01-31 , DOI: 10.1002/met.1973
Ruth Coughlan ₁ , Francesca Di Giuseppe ₁ , Claudia Vitolo ₁ , Christopher Barnard ₁ , Philippe Lopez ₁ , Matthias Drusch ₂

Affiliation

Lightning‐caused wildfires are a significant contributor to burned areas, with lightning ignitions remaining one of the most unpredictable aspects of the fire environment. There is a clear connection between fuel moisture and the probability of ignition; however, the mechanisms are poorly understood and predictive methods are underdeveloped. Establishing a lightning–ignition relationship would be useful in developing a model that would complement early warning systems designed for fire control and prevention. A machine learning (ML) approach was used to define a predictive model for wildfire ignition based on lightning forecasts and environmental conditions. Three different binary classifiers were adopted: a decision tree, an AdaBoost and a Random Forest, showing promising results, with both ensemble methods (Random Forest and AdaBoost) exhibiting an out‐of‐sample accuracy of 78%. Data provided by a Western Australia wildfire database allowed a comprehensive verification on over 145 lightning‐ignited wildfires in regions of Australia during 2016. This highlighted that in a minimum of 71% of the cases the ML models correctly predicted the occurrence of an ignition when a fire was actually initiated. The super‐learner developed is planned to be used in an operational context to the enhance information connected to fire management.

中文翻译：

使用机器学习从闪电预报中预测着火事件

雷电引起的野火是造成燃烧区域的重要原因，雷电仍然是火灾环境中最不可预测的方面之一。燃油水分和着火可能性之间有明确的联系；然而，机制了解甚少，预测方法还不完善。建立雷电与点火的关系将有助于开发一种模型，该模型可补充旨在控制和预防火灾的预警系统。基于闪电预报和环境条件，使用了机器学习（ML）方法来定义野火点火的预测模型。采用了三种不同的二元分类器：决策树，AdaBoost和随机森林，显示出令人鼓舞的结果，两种集成方法（Random Forest和AdaBoost）均显示出78％的样本外准确性。西澳大利亚州野火数据库提供的数据可以对2016年澳大利亚地区超过145种闪电点燃的野火进行全面验证。这突显出，在至少71％的情况下，ML模型可以正确地预测当实际上起火了。计划在操作环境中使用开发的超级学习器，以增强与消防管理有关的信息。这突显出，在至少71％的情况下，ML模型正确预测了实际着火时点火的发生。计划在操作环境中使用开发的超级学习器，以增强与消防管理有关的信息。这突显出，在至少71％的情况下，ML模型正确预测了实际着火时点火的发生。计划在操作环境中使用开发的超级学习器，以增强与消防管理有关的信息。

更新日期：2021-02-01

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