Investor Presentaiton
CC
Conclusions
Higher class match with the observed burned areas, which are closer to the
coast in summer and develop along the interior in winter.
• The RF algorithm seems to achieve good performances and has the advantage
of being able to extract knowledge and insights from data, and developing
promising ranking of variables and categorical classes.
⚫ In summary, RF seems to be a promising alternative to deterministic or
statistical expert-based method for wildfire susceptibility mapping.
BIBLIOGRAPHY
Tonini, M.; D'Andrea, M.; Biondi, G.; Degli Esposti, S.; Trucchia, A.; Fiorucci, P. A
Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case
Study of the Liguria Region in Italy. Geosciences 2020, 10, 105.
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BY
WILDFIRE SUSCEPTIBILITY MAPPING IN LIGURIA (ITALY).
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