Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

Abstract

To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of landslides more difficult. In some cases, landslide inventories report information on the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis. Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be related to different triggers. To do so, we built two landslide susceptibility models and observed that the spatial patterns of susceptibility were completely distinct. We found that these patterns were correlated with specific controlling factors, suggesting that active landslides are regulated by current rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The importance of this result resides in that we obtained it with a purely data-driven approach, and this was possible because the active/relict landslide classification in the inventory was accurate.

Landslide Movement of Bendungan District Trenggalek Using an Artificial Neural Network

“…Landslides are the result of complex factors, including various causes and triggers (Loche et al, 2022;McColl, 2022). Factors that affect landslides in this study are slope, soil texture, soil wrinkle index, rainfall, topography position index, and land cover (Miswar et al, 2022;Nugraha et al, 2022).…”

Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory

“…Das et al (2012) show one example of Bayesian GLM to assess the landslide susceptibility in the proximity of roads in a Indian case study. Analogous examples can be found more recently at catchment (Lombardo et al, 2020b;Luo et al, 2021) and regional scale assessments (Tanyas et al, 2021;Loche et al, 2022a). Moreover, Lombardo et al (2018aLombardo et al ( , 2019 proposed an extension of the Bayesian workflow pursued by the authors mentioned above by using a Log-Gaussian Cox Process to predict landslide counts per mapping unit, this being implemented in R-INLA (Lindgren and Rue, 2015;Bakka et al, 2018).…”

Modeling the size of co-seismic landslides via data-driven models: the Kaikōura's example

“…Slope steepness Slope (Zevenbergen and Thorne, 1987) Northness NN e.g., (Loche et al, 2022) Eastness EN e.g., (Loche et al, 2022) Local relief Relief (Jasiewicz and Stepinski, 2013) Profile curvature PRC (Heerdegen and Beran, 1982) Planar curvature PLC (Heerdegen and Beran, 1982) Euclidean distance to road Dist2R e.g., (Lepore et al, 2012) Peak ground acceleration g (m/s 2 ) (Worden and Wald, 2016) Soil depth Soil Depth (Webb and Lilburne, 2011;Hewitt et al, 2010;Lepore et al, 2012) metric function based on a slope angle map (grid cell resolution 12.5 x 12.5 m) was used to derive the "true" surface area of each landslide polygon in analogy to Steger et al (2021). As for the landscape characteristics, we computed the mean and standard deviation of the each continuous covariate per SU.…”