Disturbances such as forest fires, intense winds, and insect damage exert strong impacts on forest ecosystems by shaping their structure and growth dynamics, with contributions from climate change. Consequently, there is a need for reliable and operational methods to monitor and map these disturbances for the development of suitable management strategies. While susceptibility assessment using machine learning methods has increased, most studies have focused on a single disturbance. Moreover, there has been limited exploration of the use of “Automated Machine Learning (AutoML)” in the literature. In this study, susceptibility assessment for multiple forest disturbances (fires, insect damage, and wind damage) was conducted using the PyCaret AutoML framework in the Izmir Regional Forest Directorate (RFD) in Turkey. The AutoML framework compared 14 machine learning algorithms and ranked the best models based on AUC (area under the curve) values. The extra tree classifier (ET) algorithm was selected for modeling the susceptibility of each disturbance due to its good performance (AUC values > 0.98). The study evaluated susceptibilities for both individual and multiple disturbances, creating a total of four susceptibility maps using fifteen driving factors in the assessment. According to the results, 82.5% of forested areas in the Izmir RFD are susceptible to multiple disturbances at high and very high levels. Additionally, a potential forest disturbances map was created, revealing that 15.6% of forested areas in the Izmir RFD may experience no damage from the disturbances considered, while 54.2% could face damage from all three disturbances. The SHAP (Shapley Additive exPlanations) methodology was applied to evaluate the importance of features on prediction and the nonlinear relationship between explanatory features and susceptibility to disturbance.

森林火灾、强风和虫害等干扰因素通过塑造森林生态系统的结构和生长动态，对森林生态系统产生强烈影响，其中气候变化也起到了一定作用。因此，需要可靠且可操作的方法来监测和绘制这些干扰，以制定合适的管理策略。虽然使用机器学习方法进行的敏感性评估有所增加，但大多数研究都集中在单一干扰上。此外，文献中对“自动机器学习（AutoML）”的使用探索有限。在本研究中，使用土耳其伊兹密尔地区森林局 (RFD) 的 PyCaret AutoML 框架对多种森林干扰（火灾、虫害和风害）进行了敏感性评估。 AutoML 框架比较了 14 种机器学习算法，并根据 AUC（曲线下面积）值对最佳模型进行了排名。由于其良好的性能（AUC 值> 0.98），选择额外的树分类器（ET）算法来对每种干扰的敏感性进行建模。该研究评估了个体和多重干扰的敏感性，在评估中使用十五个驱动因素创建了总共四个敏感性图。根据结果​​，伊兹密尔 RFD 82.5% 的森林地区容易受到高强度和极高强度的多重干扰。此外，还创建了潜在的森林干扰地图，显示伊兹密尔 RFD 的 15.6% 的森林地区可能不会受到所考虑的干扰的损害，而 54.2% 的森林地区可能会面临所有三种干扰的损害。采用SHAP（Shapley Additive exPlanations）方法来评估特征对预测的重要性以及解释特征与干扰敏感性之间的非线性关系。