Using machine learning to predict fire‐ignition occurrences from lightning forecasts. (31st January 2021)

• Record Type: Journal Article
• Authors: Coughlan, Ruth; Di Giuseppe, Francesca; Vitolo, Claudia; Barnard, Christopher; Lopez, Philippe; Drusch, Matthias
• Is Part Of: Meteorological applications. Volume 28:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-31
• Subjects: AdaBoost -- classifier -- decision tree -- lightning -- lightning ignition -- machine learning -- Random Forest -- wildfire; Meteorology -- Periodicals; Meteorological services -- Periodicals; 551.5
• DOI: 10.1002/met.1973
• Languages: English
• ISSNs: 1350-4827

Abstract

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. Abstract : 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, a Random Forest and an AdaBoost, showing promising results, with both ensemble methods (Random Forest and AdaBoost) exhibiting an out‐of‐sample accuracy of 78%.