Abstract Landslide susceptibility mapping has become one of the most important tools for the management of landslide hazards. In this study, we proposed a novel approach to improve the performance of Credal Decision Tree (CDT) by using four ensemble frameworks: Bagging, Dagging, Decorate, and Rotation Forest (RF) for landslide susceptibility mapping. A total number of 180 past and present landslides data of the Muong Lay district (Viet Nam) was analyzed and used for generating training and validation of the models. Several standard statistical performance evaluation metrics, such as negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, Kappa, Area Under the receiver operating Characteristic curve (AUC) were used to evaluate performance of the models. Results indicated that all the developed and applied models performed well (AUC: 0.842-0.886) but performance of the RF-CDT (AUC: 0.886) model is the best. Therefore, the RF-CDT ensemble model can be used for the correct landslide susceptibility mapping and for proper landslide management not only of the study area but also other hilly areas of the world.

中文翻译：

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基于 GIS 的滑坡敏感性绘图集成软计算模型

摘要 滑坡敏感性测绘已成为滑坡灾害管理的重要工具之一。在这项研究中，我们提出了一种通过使用四种集成框架来提高 Credal 决策树 (CDT) 性能的新方法：Bagging、Dagging、Decorate 和 Rotation Forest (RF) 用于滑坡敏感性绘图。对 Muong Lay 区（越南）过去和现在的 180 个滑坡数据进行了分析，并用于生成模型的训练和验证。几种标准的统计性能评估指标，如阴性预测值、阳性预测值、均方根误差、准确性、灵敏度、特异性、Kappa、接受者操作特征曲线下面积（AUC）被用于评估模型的性能。结果表明，所有开发和应用的模型都表现良好（AUC：0.842-0.886），但 RF-CDT（AUC：0.886）模型的性能最好。因此，RF-CDT 集合模型可用于正确的滑坡敏感性绘图和适当的滑坡管理，不仅适用于研究区，也适用于世界其他丘陵地区。