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

Meteorological applications

article

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

[1]European Centre for Medium-range Weather Forecasts (ECMWF)

[2]European Space Agency-European Space Research and Technology Centre (ESA-ESTEC)

关键词: Pediatrics;    Risk Factors;    Neurogenic Bladder"/>;   

DOI  :  10.1002/met.1973

学科分类：社会科学、人文和艺术（综合）

来源: Wiley

【 摘 要 】

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】

CC BY|CC BY-NC|CC BY-NC-ND