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.
Highlights
Landslide susceptibility models (LSM) can be used to predict the spatial occurrence of future landslides by assuming, consistently with the uniformitarian principle, that in any given area, slope failures will occur under the same circumstances and because of the same conditions that caused them in the past
Controls and Fate of Active Landslides. It emerged that, consistently with the definition of relict landslides, the conditions that caused their occurrence in the past are distinct from those that are responsible for the active movements in the present
The analyses presented in this work aimed at investigating differences in the spatial patterns of relict and active landslides in a landslide-rich geomorphological context
Summary
Academic Editor: Stefano MorelliData-driven models can be thought as empirical tools that extract functional relationships from past phenomena to estimate the expected behaviour of the same phenomena in a pre-defined (or ill-defined) future. Hutton first and subsequently Lyell helped to develop and spread the concept of uniformitarianism, replacing the prevailing idea of catastrophism Since this concept has formed the backbone of any landslide susceptibility study [3]. Landslide susceptibility models (LSM) can be used to predict the spatial occurrence of future landslides by assuming, consistently with the uniformitarian principle, that in any given area, slope failures will occur under the same circumstances and because of the same conditions that caused them in the past. This principle may not always hold true [4]. Changes in material properties are reflected in morphological changes which, in turn, affect the process dynamics [6]
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