@article{ref1,
title="Using machine learning to predict fire-ignition occurrences from lightning forecasts",
journal="Meteorological applications",
year="2021",
author="Coughlan, Ruth and Di Giuseppe, Francesca and Vitolo, Claudia and Barnard, Christopher and Lopez, Philippe and Drusch, Matthias",
volume="28",
number="1",
pages="e1973-e1973",
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.<p /> <p>Language: en</p>",
language="en",
issn="1350-4827",
doi="10.1002/met.1973",
url="http://dx.doi.org/10.1002/met.1973"
}