TY  - JOUR
PY  - 2021//
TI  - Using machine learning to predict fire-ignition occurrences from lightning forecasts
JO  - Meteorological applications
A1  - Coughlan, Ruth
A1  - Di Giuseppe, Francesca
A1  - Vitolo, Claudia
A1  - Barnard, Christopher
A1  - Lopez, Philippe
A1  - Drusch, Matthias
SP  - e1973
EP  - e1973
VL  - 28
IS  - 1
N2  - 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>
LA  - en
SN  - 1350-4827
UR  - http://dx.doi.org/10.1002/met.1973
ID  - ref1
ER  -