阿尔及利亚是受野火影响最严重的马格里布国家之一。经济,环境,这些火灾的社会后果可能会在野火后持续数年。通常,如果对火灾爆发的检测足够快,就有可能避免此类灾难,可靠,和早期。由于数据集的缺乏,将预测阿尔及利亚野火的方法限制在测绘风险区域,每年更新。这项研究是最新数据集的结果,该数据集与2012年Bejaia和SidiBel-Abbes的森林火灾历史有关。数据集规模很小,我们使用主成分分析将变量的数量减少到6个,同时保留了总方差的96.65%。此外,我们开发了一个带有两个隐藏层的人工神经网络(ANN)来预测这些城市的野火。接下来,我们训练了分类器的性能,并将其与Logistic回归提供的性能进行了比较,K最近的邻居,支持向量机,和随机森林分类器,使用10倍分层交叉验证。实验表明,与其他分类器相比,人工神经网络分类器略有优势,在准确性方面,精度,和回忆。我们的分类器达到0.967±0.026的准确度和0.971±0.023的F1评分。SHAP技术揭示了特征的重要性(RH,DC,ISI)在人工神经网络模型的预测中。
Algeria is one of the Maghreb countries most affected by wildfires. The economic, environmental, and societal consequences of these fires can last several years after the wildfire. Often, it is possible to avoid such disasters if the detection of the outbreak of fire is fast enough, reliable, and early. The lack of datasets has limited the methods used to predict wildfires in Algeria to the mapping risk areas, which is updated annually. This study is the result of the availability of a recent dataset relating the history of forest fires in the cities of Bejaia and Sidi Bel-Abbes during the year 2012. The dataset being small size, we used principal component analysis to reduce the number of variables to 6, while retaining 96.65% of the total variance. Moreover, we developed an artificial neural network (ANN) with two hidden layers to predict wildfires in these cities. Next, we trained and compared the performance of our classifier with those provided by the Logistic Regression, K Nearest Neighbors, Support Vector Machine, and Random Forest classifiers, using a 10-fold stratified cross-validation. The experiment shows a slight superiority of the ANN classifier compared to the others, in terms of accuracy, precision, and recall. Our classifier achieves an accuracy of 0.967±0.026 and F1-score of 0.971±0.023. The SHAP technique revealed the importance of the features (RH, DC, ISI) in the predictions of the ANN model.