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A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

机译：一种基于机器学习的野火敏感性映射方法。意大利利古里亚地区的案例研究

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Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period (1997–2017) and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.
机译：野火易感性图显示了未来燃烧区域的空间概率，仅基于网站的内在本地局部。目前在该领域的研究通常依赖于统计模型，通常通过专业知识来改善数据检索和处理。在过去的几年里，由于他们通过隐藏关系的建模学习了他们的学习能力，因此已经证明了机器学习算法在这个域中成功。在本研究中，作者介绍了一种基于随机森林的方法，允许阐述意大利利古里亚地区的野火易感性图。由于浓密和异质植被，该地区受野火的影响很大，森林覆盖了超过70％的表面，并且由于气候条件有利。通过考虑映射的火势周长的数据集，跨越21年（1997-2017）和不同地理环境预测因素（即陆地覆盖，植被类型，道路网络，高度和衍生物）来评估易感性。一个主要目的是比较不同的模型，以评估：（i）包括或者不包括邻近植被类型作为额外的诱导因子和（ii）使用空间交叉验证程序中的倍数倍数。最终详细说明并验证了两种火灾季节的易感性图。结果强调了拟议方法识别可能在不久的将来受野火影响的地区的能力，以及评估消防活动效率的善良。

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