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

Meteorological Applications

Claudia Vitolo¹  Philippe Lopez¹  Ruth Coughlan¹  Christopher Barnard¹  Francesca Di Giuseppe¹  Matthias Drusch² 

[1] European Centre for Medium‐range Weather Forecasts (ECMWF) Reading UK;European Space Agency‐European Space Research and Technology Centre (ESA‐ESTEC) Noordwijk the Netherlands;

关键词: AdaBoost;    classifier;    decision tree;    lightning;    lightning ignition;    machine learning;   

DOI  :  10.1002/met.1973

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown