LAUSR

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.