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Using machine learning to predict fire-ignition occurrences from lightning forecasts.
- Source :
-
Meteorological Applications . Jan/Feb2021, Vol. 28 Issue 1, p1-16. 16p. - Publication Year :
- 2021
-
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-ofsample 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13504827
- Volume :
- 28
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Meteorological Applications
- Publication Type :
- Academic Journal
- Accession number :
- 148795645
- Full Text :
- https://doi.org/10.1002/met.1973