Identifying locations of landslide slip surfaces provides critical information for understanding the volume of landslides and the scale of disasters, both of which are essential for formulating disaster preparedness and mitigation strategies. Based on hydrogeological survey data from 24 deep-seated landslide-prone sites in Taiwan’s mountainous regions, this study developed the hydraulic conductivity potential index (HCPI) using principal component analysis to quantify the hydraulic properties of disturbed rock formations with six geological factors. Then, regression analysis was performed to construct a permeability estimation model for the geological environment of landslides. Finally, the established model was utilized to develop an identification method for potential slip depths in landslide-prone sites. Results indicated a strong relation between HCPI and hydraulic conductivity with a determination coefficient of 0.895. The relation equation confirmed that the data it generated concerning the depths of significant changes in hydraulic conductivity could be used to identify potential slip surfaces. Additionally, this study successfully established a rule for identifying potential slip zones by summarizing data concerning the generated hydraulic conductivity profiles, stratigraphic lithology, existing inclinometer slip depth records, and groundwater level of landslide sites. This identification method was then applied to predict potential slip depths for ten landslide sites where slip surfaces have not yet occurred. These findings offer a new alternative to having early information on potential sliding depths for timely disaster management and control implementation.
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