Abstract
AbstractSimilar 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.
Funder
Eusko Jaurlaritza
Universidad del País Vasco
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Reference91 articles.
1. Akinci H, Zeybek M (2021) Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey. Nat Hazards 108:1515–1543. https://doi.org/10.1007/s11069-021-04743-4
2. Alvioli M, Marchesini I, Reichenbach P, Rossi M, Ardizzone F, Fiorucci F, Guzzetti F (2016) Automatic delineation of geomorphological slope units with r. slope units v1. 0 and their optimization for landslide susceptibility modeling. Geosci Model Dev 9:3975–3991. https://doi.org/10.5194/gmd-9-3975-2016
3. Alvioli M, Mondini AC, Fiorucci F, Cardinali M, Marchesini I (2018) Topography-driven satellite imagery analysis for landslide mapping. Geomat Nat Hazards Risk 9:544–567. https://doi.org/10.1080/19475705.2018.1458050
4. Bernat Gazibara S, Krka M, Mihali Arbanas S (2019) Verification of historical landslide inventory maps for the Podsljeme area in the City of Zagreb using LiDAR-based landslide inventory. Min Geol Pet Eng Bull 34:45–58. https://doi.org/10.17794/rgn.2019.1.5
5. Bonachea J, Remondo J, Rivas V, Sánchez Espeso J, Bruschi VM, Cendrero A, Díaz de Terán JR, Fernández Maroto G, Gómez Arozamena J, González-Díez AA, Sainz C (2016) Desarrollo de escenarios de peligrosidad y riesgo por deslizamientos (proyecto Espérides). In: Durán Valsero JJ et al. (ed) Comprendiendo el relieve: del pasado al futuro: actas de la XIV Reunión Nacional de Geomorfología Málaga. Instituto Geológico y Minero de España, pp 205–212
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献