Open AccessUsing machine learning to predict fire‐ignition occurrences from lightning forecasts
Author(s) -
Coughlan Ruth,
Di Giuseppe Francesca,
Vitolo Claudia,
Barnard Christopher,
Lopez Philippe,
Drusch Matthias
Publication year - 2021
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1973
Subject(s) - lightning (connector) , context (archaeology) , ignition system , lightning detection , adaboost , environmental science , computer science , random forest , decision tree , flammability , meteorology , machine learning , support vector machine , geography , engineering , thunderstorm , power (physics) , physics , archaeology , quantum mechanics , aerospace engineering , thermodynamics
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.