Abstract

Given the increasing occurrence of landslides worldwide, the improvement of predictive models for landslide mapping is needed. Despite the influence of geotechnical parameters on SHALSTAB model outputs, there is a lack of research on models’ performance when considering different variables. In particular, the role of geotechnical units (i.e., areas with common soil and lithology) is understudied. Indeed, the original SHALSTAB model considers that the whole basin has homogeneous soil. This can lead to the under-or-overestimation of landslide hazards. Therefore, in this study, we aimed to investigate the advantages of incorporating geotechnical units as a variable in contrast to the original model. By using locally sampled geotechnical data, 13 slope-instability scenarios were simulated for the Jaguar creek basin, Brazil. This allowed us to verify the sensitivity of the model to different input variables and assumptions. To evaluate the model performance, we used the Success Index, Error Index, ROC curve, and a new performance index: the Detective Performance Index of Unstable Areas. The best model performance was obtained in the scenario with discretized geotechnical units’ values and the largest sample size. Results indicate the importance of properly characterizing the geotechnical units when using SHALSTAB. Hence, future applications should consider this to improve models’ predictivity.

Highlights

  • Landslide disasters have increased significantly in the last decades, leading to economic disruption, damage to properties, and loss of lives

  • The present study showed the effect of geotechnical unit consideration on mapping performance with SHALSTAB, it did not investigate how uncertainties in parameter determination affect the reliability of the results

  • The present study demonstrated the importance of considering the geotechnical-unit characterization on landslide mapping performance by considering the case of the Jaguar creek basin, southern Brazil

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Summary

Introduction

Landslide disasters have increased significantly in the last decades, leading to economic disruption, damage to properties, and loss of lives. According to the EM-DAT database, landslides claimed the lives of 26,750 people worldwide from 1991 to 2020. In Brazil, 629 and 2502 fatalities were reported due to landslides and due to the combination of landslides and floods, respectively. To reduce the existing risk, public managers have long been adopting tools to subsidize decision-making by optimizing regional planning [2]. To this end, delineating landslide-prone areas is considered essential, as these assessments provide decision-makers with relevant information regarding landslide occurrences [3,4]

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