Abstract Ensemble learning methods can be used to evaluate landslide susceptibility when combined with remote sensing (RS) and geographic information systems (GIS). In this study, the rotation forest (ROF) and random forest (RF) ensemble learning models were applied to evaluate landslide susceptibility. The experiments selected the factors by analysing the linear relationship between the factors, explored the optimal proportions of non-landslide samples and landslide samples based on an unbalanced sample dataset, and used the factors before and after the selection to generate landslide susceptibility maps (LSMs) in the Zigui-Badong area. The results show that a suitable ratio between the sample types in the training set can achieve good results for both sensitivity and specificity. The RF models of the study area with 21 factors and 16 factors had sensitivities of 94.22% and 93.59%, respectively. The ROF models with 21 factors and 16 factors had sensitivities of 90.63% and 88.84%, respectively. Although both the RF and ROF models exhibited high accuracy, the RF model achieved a more reasonable and accurate LSM.

中文翻译：

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使用集成学习的旋转和随机森林模型预测滑坡敏感性

摘要 结合遥感（RS）和地理信息系统（GIS），集成学习方法可用于评估滑坡敏感性。在这项研究中，旋转森林（ROF）和随机森林（RF）集成学习模型被应用于评估滑坡敏感性。实验通过分析因子之间的线性关系来选择因子，基于不平衡样本数据集探索非滑坡样本和滑坡样本的最佳比例，并利用选择前后的因子生成滑坡敏感性图（LSMs）在秭归-巴东地区。结果表明，训练集中样本类型之间合适的比例可以在灵敏度和特异性方面取得良好的效果。研究区21个因子和16个因子的RF模型的灵敏度分别为94.22%和93.59%。21个因子和16个因子的ROF模型的灵敏度分别为90.63%和88.84%。虽然 RF 和 ROF 模型都表现出较高的精度，但 RF 模型实现了更合理和准确的 LSM。