Recent decades have seen an increase in wildfires activity, posing risks to human settlements, and forcing exploration of new technologies for wildfire risk management. Utilizing Machine Learning in Time Series classification, this study produces decision support maps for Civil Protection system in Italy, which is responsible for coordinating national firefighting air fleet. Trained on past events data, the model gives daily indication on wildfire occurrence and aerial support requests for each administrative unit utilizing time series of Forest Fire Danger Rating indexes from RISICO model. Despite its recent implementation, it performed properly in 2023, showcasing model’s potential for decision support.
Machine Learning-Driven Dynamic Maps Supporting Wildfire Risk Management
Nicolo Perello;Giorgio Meschi;Andrea Trucchia;Mirko D'Andrea;Francesco Baghino;Paolo Fiorucci
2024-01-01
Abstract
Recent decades have seen an increase in wildfires activity, posing risks to human settlements, and forcing exploration of new technologies for wildfire risk management. Utilizing Machine Learning in Time Series classification, this study produces decision support maps for Civil Protection system in Italy, which is responsible for coordinating national firefighting air fleet. Trained on past events data, the model gives daily indication on wildfire occurrence and aerial support requests for each administrative unit utilizing time series of Forest Fire Danger Rating indexes from RISICO model. Despite its recent implementation, it performed properly in 2023, showcasing model’s potential for decision support.
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