Chapter 13 References

The research framework that inspired this computational lab refers to a pioneering study in susceptibility mapping for wildfire events by11 and further developed for the assessment of variable importance by.12

References

Tonini, Marj, Mirko D’Andrea, Guido Biondi, Silvia Degli Esposti, Andrea Trucchia, and Paolo Fiorucci. “A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy.” Geosciences 10, no. 3 (March 2020): 105. https://doi.org/10.3390/geosciences10030105.
Trucchia, Andrea, Hamed Izadgoshasb, Sara Isnardi, Paolo Fiorucci, and Marj Tonini. “Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility.” Geosciences 12, no. 11 (November 2022): 424. https://doi.org/10.3390/geosciences12110424.

  1. Marj Tonini et al., “A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy,” Geosciences 10, no. 3 (March 2020): 105, https://doi.org/10.3390/geosciences10030105.↩︎

  2. Andrea Trucchia et al., “Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility,” Geosciences 12, no. 11 (November 2022): 424, https://doi.org/10.3390/geosciences12110424.↩︎