Abstract In this paper, we present a suitable integration of discrete and continuous data in a unique methodology based on systematically collected landslide inventory data. Eleven landslide conditioning factors were analyzed and used, where eight correspond to DEM–derived variables, and three to thematic polygon–type variables (shallow geology, geomorphology and soil land–use). Principal Component Analysis (PCA) was used to avoid the effect of multicollinearity. Additionally, 3 proposals were developed using Logistic Regression (LR) and Weights of Evidence (WoE) methods that use the continuous and discrete variables efficiently, respectively. The performance of each proposal was evaluated by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curves. The analysis indicated that Proposal 1 with AUC = 0.8578 and Proposal 2 with AUC = 0.8459 have the best LSI assessment, while the performance of Proposal 3 with AUC = 0.8054 shows the lowest prediction approaches. In comparison with the WoE method, our proposal shows an increase in high and very high susceptibility in areas with complex topography, which is consistent with the reported landslides.

中文翻译：

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基于逻辑回归和证据权重整合的滑坡敏感性指数：哥伦比亚波帕扬的案例研究

摘要 在本文中，我们以系统收集的滑坡清单数据为基础，以独特的方法提出了离散和连续数据的适当集成。分析和使用了 11 个滑坡条件因子，其中 8 个对应于 DEM 衍生变量，3 个对应于专题多边形类型变量（浅层地质、地貌和土壤土地利用）。使用主成分分析 (PCA) 来避免多重共线性的影响。此外，还使用逻辑回归 (LR) 和证据权重 (WoE) 方法开发了 3 个提案，分别有效地使用了连续变量和离散变量。每个提案的性能由接收者操作特征 (ROC) 曲线的曲线下面积 (AUC) 评估。分析表明，AUC = 0 的建议 1。8578 和 AUC = 0.8459 的建议 2 具有最佳的 LSI 评估，而 AUC = 0.8054 的建议 3 的性能显示最低的预测方法。与 WoE 方法相比，我们的建议显示复杂地形区域的高和非常高的易感性增加，这与报告的滑坡一致。