Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data..

Publication:
Alvioli M, Loche M, Jacobs L, Grohmann CH, Abraham MT et al. (2024): A benchmark 
dataset and workflow for landslide susceptibility zonation. Earth-Science Reviews, Volume 258. 
https://doi.org/10.1016/j.earscirev.2024.104

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Publication: Šilhán K., Fabiánová A., Klimeš J., Tábořík P., Hartvich F., Blahůt J. (2023): The effect of tree growth disturbances inertia on dendrogeomorphic spatio-temporal analysis of landslides: A case study. Catena 235, 107678. 
doi.org/10.1016/j.catena.2023.107678

Publication: Petr Kolář, Matěj Petružálek (2024): Discrimination of doubled acoustic emission events using neural networks. Ultrasonics. Vol. 144, Dec. (2024), no. 107439. ISSN 0041-624X. E-ISSN 1874-9968, DOI: https://doi.org/10.1016/j.ultras.2024.107439

The San Andrés landslide on El Hierro (Canary Islands) represents a rare opportunity to study an incipient volcanic island flank collapse with an extensive onshore part. The presented research improves the knowledge of the internal structure and rock characteristics of a mega-landslide before its complete failure. The onshore geophysical investigations helped detect the possible San Andrés landslide sliding surfaces at depths between 320 m and 420 m, with a rather planar geometry and they also revealed that rocks inside and outside of the landslide had similar properties which suggests that the previous fast movements of the landslide did not affect the bulk properties of the displaced rocks.
Publication:
Klimeš, J.; Hussain, Y.; Mreyen, A.-S.; Cauchie, L.; Schlögel, R.; Piroton, V.; Petružálek, M.; Blahůt, J.; René, M.; Meletlidis, S.; Havenith, H.-B. (2023) New Insights into the Internal Structures and Geotechnical Rock Properties of the Giant San Andrés Landslide, El Hierro Island, Spain. Remote Sens., 15, 1627. https://doi.org/10.3390/rs15061627

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Publication: Duque, J., Loche, M., & Scaringi, G. (2023). Rate-dependency of residual shear strength of soils: implications for landslide evolution. Géotechnique Letters, 13(2),1-8. https://doi.org/10.1680/jgele.23.00004

Publication: Racek O., Balek J., Loche M., Vích D., Blahůt J. (2023) Rock Surface Strain In Situ Monitoring Affected by Temperature Changes at the Požáry Field Lab (Czechia). Sensors 23(4), 2237. https://doi.org/10.3390/s23042237

Článek popisuje univerzální, modulární a finančně dostupný systém monitoringu skalního svahu. Pomocí tohoto systému, je možné in-situ pozorovat klimatické proměnné, teplotu v povrchové vrstvě skalního svahu až do hloubky 3 m a dynamiku vybraných diskontinuit v čase. První výsledky ukazují na rozdíly pozorované mezi jednotlivými lokalitami. Pro budoucí rozsáhlejší statistickou analýzu dat, budou použity delší časové řady. Dále budou data využita pro analýzu dlouhodobých trendů a numerické termomechanické modelování.

Racek, O., Blahůt, J., and Hartvich, F. (2021): Observation of the rock slope thermal regime, coupled with crackmeter stability monitoring: initial results from three different sites in Czechia (central Europe), Geosci. Instrum. Method. Data Syst., 10, 203–218, 
DOI: 10.5194/gi-10-203-2021