Hit but Not Overwhelmed. A Model to Capture the Efficacy of Distance Learning: The Report From Regione Liguria (Italy)

Sugliano, Angela Maria; Cavallini, M. Felicia

doi:10.21125/iceri.2021.1352

Cited by 1 publication

(2 citation statements)

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“…The input dataset used in the present study is the same one used in [15], consisting of mapped burned areas spanning a 21-year period (1997-2017) (Figure 2), provided by the regional forestry service [46]. This wildfire inventory was created based on GPS-surveying and subsequent digitalization over the cadastral map (scale 1:10,000) and includes the starting date of each event.…”

Section: Wildfires Datasetmentioning

confidence: 99%

“…The second group includes the Euclidean distances to an anthropogenic features (i.e., urban areas, road networks, pathways, and crops), and if a pixel belongs or not to a protected area. All this digital information has been provided by the Authority of the Liguria Region and is available at the official geo-portal (https://geoportal.regione.liguria.it/, accessed on 17 November 2022) [46]; a detailed description can be found in [15]. All the spatial layers were rasterized, re-sampled, and spatially aligned to match the same reference raster with a spatial resolution of 100 m.…”

Section: Predictor Variablesmentioning

confidence: 99%

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Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility

et al. 2022

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Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) and recently adapted to the national scale. In these previous works, a random-forest-based modeling workflow was developed to assess susceptibility to wildfires under the influence of a number of environmental predictors. The main novelties and contributions of the present study are: (i) we compared models based on random forest, multi-layer perceptron, and support vector machine, to estimate their prediction capabilities; (ii) we used a more accurate vegetation map as predictor, allowing us to evaluate the impacts of different types of local and neighboring vegetation on wildfires’ occurrence; (iii) we improved the selection of the testing dataset, in order to take into account the temporal variability of the burning seasons. Wildfire susceptibility maps were finally created based on the output probabilistic predicted values from the three machine-learning algorithms. As revealed with random forest, vegetation is so far the most important predictor variable; the marginal effect of each type of vegetation was then evaluated and discussed.

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Section: Wildfires Datasetmentioning

confidence: 99%

Section: Predictor Variablesmentioning

confidence: 99%