Analysis of landslide susceptibility prediction accuracy with an event-based inventory: The 6 February 2023 Turkiye earthquakes


Karakas G., Unal E. O., Cetinkaya S., Ozcan N. T., Karakas V. E., Can R., ...More

Soil Dynamics and Earthquake Engineering, vol.178, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 178
  • Publication Date: 2024
  • Doi Number: 10.1016/j.soildyn.2024.108491
  • Journal Name: Soil Dynamics and Earthquake Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: 6 February 2023 kahramanmaras earthquakes (Turkiye), Aerial photogrammetry, Co-seismic landslides, Random forest, Susceptibility mapping
  • Hacettepe University Affiliated: Yes

Abstract

Landslide susceptibility assessment is a complex challenge explored by various scientists, but not fully resolved. In this study, we produced the landslide susceptibility map of a large region covering 38,500 km2 area in South-East Turkiye severely affected by the 6 February 2023 Kahramanmaras Earthquakes (Mw 7.7 and Mw 7.6) using an inventory produced in previous years. We employed random forest regression with a total of nine geomorphological and environmental features and evaluated the results using the co-seismic inventory with 2611 landslides compiled here. Although high accuracy was obtained from pixel-based assessments of test data split from the learning set, the independent validation set of co-seismic landslides showed that attention needs to be paid unseen features such as rare lithological units. Given the significant damage caused by latent hazards with the Kahramanmaras earthquakes, producing reliable inventories and precise landslide susceptibility maps is crucial for risk reduction and minimizing damages.