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Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain)

Author

Listed:
  • Txomin Bornaetxea

    (Universidad del País Vasco (UPV/EHU))

  • Juan Remondo

    (Universidad de Cantabria)

  • Jaime Bonachea

    (Universidad de Cantabria)

  • Pablo Valenzuela

    (Universidad de Cantabria)

Abstract

Similar to many mountainous regions of the world, landslides are a recurrent geological hazard in the Gipuzkoa province (northern Spain) that commonly cause damage to communication infrastructure, such as roads and railways. This geomorphological process also threatens buildings and human beings, albeit to a lesser degree. Over time, different institutions and academic research groups have individually collected crucial information on historic and ancient landslides in this region, resulting in various landslide inventories. However, these inventories have not been collectively assessed, and their suitability for landslide susceptibility modelling projects has often been assumed without comprehensive evaluation. In this study, we propose a simplified method to explore, describe, and compare the various landslide inventories in a specific study area to assess their suitability for landslide susceptibility modelling. Additionally, we present the results of an illustrative experiment that demonstrates the direct effect of using different inventories in landslide susceptibility modelling through a data-driven approach. We found that out of the five digitally available inventories in the study area, only three provide sufficient guarantees to be used as input data for susceptibility modelling. Furthermore, we observed that each individual inventory exhibited inherent biases, which directly influenced the resulting susceptibility map. We believe that our proposed methods can be easily replicated in other study areas where multiple landslide inventory sources exist, and that our work will induce other researchers to conduct preliminary assessments of their inventories as a critical step prior to any landslide susceptibility modelling project.

Suggested Citation

  • Txomin Bornaetxea & Juan Remondo & Jaime Bonachea & Pablo Valenzuela, 2023. "Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2513-2542, September.
  • Handle: RePEc:spr:nathaz:v:118:y:2023:i:3:d:10.1007_s11069-023-06103-w
    DOI: 10.1007/s11069-023-06103-w
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    References listed on IDEAS

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    1. Halil Akinci & Mustafa Zeybek, 2021. "Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 1515-1543, September.
    2. Hamid Pourghasemi & Biswajeet Pradhan & Candan Gokceoglu, 2012. "Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 965-996, September.
    3. Xinfu Xing & Chenglong Wu & Jinhui Li & Xueyou Li & Limin Zhang & Rongjie He, 2021. "Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 97-117, March.
    4. Mohammad Mehrabi, 2022. "Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 901-937, March.
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