In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.

中文翻译：

---

改进投票特征间隔以预测滑坡的空间

在这项研究中，主要目的是使用两种强大的集成技术AdaBoost和MultiBoost对滑坡敏感性进行评估和预测，以提高投票特征间隔（VFI）的性能，这是最有效的机器学习模型之一。为此，开发了两种混合模型，即基于AdaBoost的投票特征区间（ABVFI）和基于MultiBoost的投票特征区间（MBVFI），并使用从越南受滑坡影响的地区之一的Muong Lay收集的滑坡数据进行了验证。 。定量验证方法包括ROC曲线下的面积（AUC）用于评估模型性能。结果表明，新开发的集成模型ABVFI（AUC = 0.859）和MBVFI（AUC = 0.839）均优于单个VFI（AUC = 0.824）模型。从而，基于整体框架的VFI算法可用于滑坡的准确空间预测，也可应用于世界上其他易发生滑坡的地区。由整体VFI模型开发的滑坡敏感性图可用于更好地预防该地区的滑坡和进行风险管理。