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

Author(s): Ruth Coughlan, Francesca Di Giuseppe, Claudia Vitolo, Christopher Barnard, Philippe Lopez, Matthias Drusch
Year Published: 2021
Description:

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.

Citation: Coughlan, Ruth; Di Giuseppe, Francesca; Vitolo, Claudia; Barnard, Christopher; Lopez, Philippe; Drusch, Matthias. 2021. Using machine learning to predict fire-ignition occurrences from lightning forecasts. Meteorological Applications 28(1):e1973. https://doi.org/10.1002/met.1973
Topic(s): Fire Behavior, Simulation Modeling, Fuels
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 22728
FRAMES RCS number: 62756
Record updated: Mar 9, 2021