Abstract

In this study, two novel hybrid models namely Bagging-based Rough Set (BRS) and AdaBoost-based Rough Set (ABRS) were used to generate landslide susceptibility maps of Son La hydropower reservoir basin, Vietnam. In total, 186 past landslide events and twelve landslides affecting factors (slope degree, slope aspect, elevation, curvature, focal flow, river density, rainfall, aquifer, weathering crust, lithology, fault density and road density) were considered in the modeling study. The landslide data was split into training (70%) and testing (30%) for the model’s development and validation. One R feature selection method was used to select and prioritize the landslide affecting factors based on their importance in model prediction. Performance of the hybrid developed models was evaluated and also compared with single rough set (RS) and support vector machine (SVM) models using various standard statistical measures including area under the curve (AUC)-receiver operating characteristics (ROC) curve. The results show that the developed hybrid model BRS (AUC = 0.845) is the most accurate model in comparison to other models (ABRS, SVM and RS) in predicting landslide susceptibility. Therefore, the BRS model can be used as an effective tool in the development of an accurate landslide susceptibility map of the hilly area.

摘要

在这项研究中，两个新的混合模型，即基于 Bagging 的粗糙集（BRS）和基于 AdaBoost 的粗糙集（ABRS）被用来生成越南 Son La 水电站水库盆地的滑坡敏感性图。模拟研究总共考虑了过去186次滑坡事件和12个滑坡影响因素（坡度、坡向、高程、曲率、震源流、河流密度、降雨量、含水层、风化壳、岩性、断层密度和道路密度） . 滑坡数据分为训练 (70%) 和测试 (30%)，用于模型的开发和验证。一种R特征选择方法用于根据滑坡影响因素在模型预测中的重要性进行选择和优先排序。使用包括曲线下面积 (AUC)-接收器操作特性 (ROC) 曲线在内的各种标准统计量度，对混合开发模型的性能进行了评估，并与单一粗糙集 (RS) 和支持向量机 (SVM) 模型进行了比较。结果表明，与其他模型（ABRS、SVM 和 RS）相比，开发的混合模型 BRS (AUC = 0.845) 是预测滑坡敏感性的最准确模型。因此，BRS 模型可作为开发精确的丘陵地区滑坡敏感性图的有效工具。845）是与其他模型（ABRS、SVM 和 RS）相比在预测滑坡敏感性方面最准确的模型。因此，BRS 模型可作为开发精确的丘陵地区滑坡敏感性图的有效工具。845）是与其他模型（ABRS、SVM 和 RS）相比在预测滑坡敏感性方面最准确的模型。因此，BRS 模型可作为开发精确的丘陵地区滑坡敏感性图的有效工具。