Abstract
Wildfires constitute an extremely serious social and environmental issue in the Mediterranean region, with impacts on human lives, infrastructures and ecosystems. It is therefore important to produce susceptibility maps for wildfire management. The wildfire susceptibility is defined as a static probability of experiencing wildfire in a certain area, depending on the intrinsic characteristics of the territory. In this work, a machine learning model based on the Random Forest Classifier algorithm is employed to obtain national scale susceptibility maps for Italy at a 500 m spatial resolution. In particular, two maps are produced, one for each specific wildfire season, the winter and the summer one. Developing such analysis at the national scale allows for having a deep understanding on the wildfire regimes furnishing a tool for wildfire risk management. The selected machine learning model is capable of associating a data-set of geographic, climatic, and anthropic information to the synoptic past burned area. The model is then used to classify each pixel of the study area, producing the susceptibility map. Several stages of validation are proposed, with the analysis of ground retrieved wildfire databases and with recent wildfire events obtained through remote sensing techniques.
Funder
Italian Civil Protection Department - Presidency of the Council of Ministers
Subject
Earth and Planetary Sciences (miscellaneous),Safety Research,Environmental Science (miscellaneous),Safety, Risk, Reliability and Quality,Building and Construction,Forestry
Reference60 articles.
1. Advance EFFIS Report on Forest Fires in Europe, Middle East and North Africa 2020https://publications.jrc.ec.europa.eu/repository/handle/JRC124833
2. Natura 2000: Protecting Europe’s Biodiversity;Sundseth,2008
3. Decreasing Fires in Mediterranean Europe
4. Advance EFFIS Report on Forest Fires in Europe, Middle East and North Africa 2019https://publications.jrc.ec.europa.eu/repository/handle/JRC120692
Cited by
35 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献