Abstract Wildfires are a major disturbance in the Mediterranean Basin and an ecological factor that constantly alters the landscape. In this context, it is crucial to understand where wildfires are more likely to occur as well as the drivers guiding them in complex landscapes such as the Mediterranean area. The objectives of this study are to estimate wildfire probability occurrence as a function of biophysical and human-related drivers, to provide an assessment of the relative impact of each driver and analyze the performance of machine learning techniques compared to traditional regression modeling. By employing an Artificial Neural Network model and fire data (2004–2012), we estimated wildfire probability across two geographical regions covering most of the Italian territory: Alpine and subalpine region and Insular and peninsular region. The high classification accuracy (0.68 for the Alpine and subalpine region and 0.76 for the Insular and peninsular region) and good performances of the technique (AUC values of 0.82 and 0.76, respectively) suggest that our model can be used in the areas studied to assess wildfire probability occurrence. We compared our model with a logistic function, which showed a weaker predictive power (AUC values of 0.78 for the Alpine and subalpine region and 0.65 for the Insular and peninsular region) compared to the Artificial Neural Network. In addition, we assessed the importance of each variable by isolating it in the model. The importance of an individual variable differed between the two regions, underscoring the high diversity of wildfire occurrence drivers in Mediterranean landscapes. Results show that in the Alpine and subalpine region, the presence of forest is the most important variable, while climate resulted as being the most important variable in the Insular and peninsular region. The majority of areas recently affected by large wildfires in both regions have been correctly classified by the ANN model as ‘high fire probability’. Hence, the use of an Artificial Neural Network is efficient and robust for understanding the probability of wildfire occurrence in Italy and other similar complex landscapes.

中文翻译：

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使用人工神经网络估计地中海景观中野火发生的概率

摘要 野火是地中海盆地的主要干扰因素，也是不断改变景观的生态因素。在这种情况下，了解野火更可能发生的位置以及在地中海地区等复杂景观中引导野火的驱动因素至关重要。本研究的目的是估计野火发生概率作为生物物理和人类相关驱动因素的函数，评估每个驱动因素的相对影响，并分析机器学习技术与传统回归模型相比的性能。通过使用人工神经网络模型和火灾数据（2004-2012），我们估计了覆盖意大利大部分领土的两个地理区域的野火概率：高山和亚高山地区以及岛屿和半岛地区。高分类准确度（高山和亚高山地区为 0.68，岛屿和半岛地区为 0.76）和该技术的良好性能（AUC 值分别为 0.82 和 0.76）表明我们的模型可用于研究的区域以评估野火概率发生。我们将我们的模型与逻辑函数进行了比较，与人工神经网络相比，逻辑函数显示出较弱的预测能力（高山和亚高山地区的 AUC 值为 0.78，岛屿和半岛地区的 AUC 值为 0.65）。此外，我们通过在模型中隔离每个变量来评估每个变量的重要性。两个区域之间单个变量的重要性不同，突显了地中海景观中野火发生驱动因素的高度多样性。结果表明，在高山和亚高山地区，森林的存在是最重要的变量，而气候是岛屿和半岛地区最重要的变量。最近这两个地区受大型野火影响的大部分地区都被 ANN 模型正确分类为“高火灾概率”。因此，人工神经网络的使用对于了解意大利和其他类似复杂景观发生野火的可能性是有效且稳健的。