Citation

Coughlan R, Di Giuseppe F, Vitolo C, Barnard C, Lopez P, Drusch M. Meterol. Appl. 2021; 28(1): e1973.

Copyright

(Copyright © 2021, John Wiley and Sons)

DOI

10.1002/met.1973

PMID

unavailable

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.

Language: en

Keywords

AdaBoost; classifier; decision tree; lightning; lightning ignition; machine learning; Random Forest; wildfire