ABSTRACT In this paper, decision trees machine learning algorithms, namely Random Forest (RF), Alternating Decision Tree (ADT), and Logistic Model Tree (LMT), were applied for modelling of susceptibility of landslides at the Luc Yen district, Northern Vietnam. These methods were evaluated to compare the performance of models and for selection of the best model for landslide susceptibility mapping and prediction. In this study, data of 95 landslides events were analysed with 10 landslide affecting factors using the Correlation-Based Feature Selection (CFS). These factors are land use, elevation, slope, distance to roads, aspect, curvature, distance to faults, rainfall, lithology, and distance to rivers. Receiver Operating Characteristic (ROC) curve, statistical indices (sensitivity, specificity, and kappa), and Chi-square test were utilised for validating and comparing the models performance. The modelling results show that the performance of RF model (AUC = 0.839) is the best with the data at hand compared to the ADT model (0.827) and the LMT (0.809) model. The RF should be applied for the better landslide susceptibility mapping and management.

中文翻译：

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使用不同高级决策树方法的滑坡敏感性建模

摘要 在本文中，决策树机器学习算法，即随机森林 (RF)、交替决策树 (ADT) 和逻辑模型树 (LMT)，被应用于越南北部 Luc Yen 区滑坡敏感性的建模。评估这些方法以比较模型的性能并选择用于滑坡敏感性绘图和预测的最佳模型。在这项研究中，使用基于相关性的特征选择 (CFS) 分析了 95 个滑坡事件的数据，其中包含 10 个滑坡影响因素。这些因素是土地利用、海拔、坡度、到道路的距离、坡向、曲率、到断层的距离、降雨量、岩性和到河流的距离。接收者操作特征 (ROC) 曲线、统计指数（敏感性、特异性和 kappa），和卡方检验用于验证和比较模型性能。建模结果表明，与 ADT 模型 (0.827) 和 LMT (0.809) 模型相比，RF 模型 (AUC = 0.839) 的性能在手头数据方面是最好的。RF 应用于更好的滑坡敏感性绘图和管理。