Natural disasters such as landslides often occur in the Eastern Black Sea region of Turkey owing to its geological, topographical, and climatic characteristics. Landslide events occur nearly every year in the Arhavi, Hopa, and Kemalpaşa districts located on the Black Sea coast in the Artvin province. In this study, the landslide susceptibility map of the Arhavi, Hopa, and Kemalpaşa districts was produced using the random forest (RF) model, which is widely used in the literature and yields more accurate results compared with other machine learning techniques. A total of 10 landslide-conditioning factors were considered for the susceptibility analysis, i.e., lithology, land cover, slope, aspect, elevation, curvature, topographic wetness index, and distances from faults, drainage networks, and roads. Furthermore, 70% of the landslides on the landslide inventory map were used for training, and the remaining 30% were used for validation. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. Evaluation results indicated that the success and prediction rates of the model were 98.3% and 97.7%, respectively. Moreover, it was determined that incorrect land-use decisions, such as transforming forest areas into tea and hazelnut cultivation areas, induce the occurrence of landslides.

中文翻译：

---

土耳其阿尔特温沿海地区基于森林的随机滑坡敏感性地图

由于其地质，地形和气候特征，土耳其东部黑海地区经常发生自然灾害，例如山体滑坡。滑坡事件几乎每年都发生在Artvin省黑海沿岸的Arhavi，Hopa和Kemalpaşa地区。在这项研究中，使用随机森林（RF）模型绘制了Arhavi，Hopa和Kemalpaşa地区的滑坡敏感性图，该模型在文献中得到了广泛使用，并且与其他机器学习技术相比，得出的结果更为准确。总共考虑了10个滑坡调节因素，即岩性，土地覆盖，坡度，纵横比，高程，曲率，地形湿度指数以及与断层，排水网络和道路的距离。此外，滑坡清单图上70％的滑坡用于训练，其余30％用于验证。使用接收器工作特性（ROC）曲线下的面积验证了基于RF的模型。评估结果表明，该模型的成功率和预测率分别为98.3％和97.7％。此外，已确定错误的土地使用决策（例如将森林地区转变为茶和榛子种植区）会导致滑坡的发生。