Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas.

森林火灾是目前世界范围内研究的热点。制定准确的策略以防止潜在影响并尽可能减少灾难性事件的发生需要对恶劣条件进行建模和预测。在这项研究中，我们开发了五种新的混合机器学习算法，即频率比多层感知器 (FR-MLP)、频率比逻辑回归 (FR-LR)、频率比分类和回归树 (FR-CART)、频率比率支持向量机 (FR-SVM) 和频率比率随机森林 (FR-RF)，用于绘制摩洛哥北部的森林火灾敏感性。为此，共有510个历史森林火灾点作为森林火灾清单图和10个独立的因果因素，包括海拔、坡度、坡向、与道路的距离、使用到住宅区的距离、土地利用、归一化差异植被指数 (NDVI)、降雨量、温度和风速。计算受试者工作特征 (ROC) 曲线 (AUC) 下的面积以评估模型的有效性。执行建议模型的结果表明，RF-FR 实现了最高性能 (AUC = 0.989)，其次是 SVM-FR (AUC = 0.959)、MLP-FR (AUC = 0.858)、CART-FR (AUC = 0.847)， LR-FR (AUC = 0.809) 在森林火灾的预测中。这项研究的成果作为森林火灾风险区的预测图，可以为地中海森林生态系统的管理提供重要支持。而且，