Articles | Volume 26, issue 11
https://doi.org/10.5194/nhess-26-5753-2026
https://doi.org/10.5194/nhess-26-5753-2026
Research article
 | 
12 Nov 2026
Research article |  | 12 Nov 2026

Predicting slope instabilities in Salvador, Brazil, using machine learning and georeferenced data

Sandro Lemos Machado, Guilherme Barbosa Santana, Miriam de Fátima Carvalho, Jailma Santos de Souza Oliveira, Mehran Karimpour-Fard, and Elio Perrone

Cited articles

Ahangari Nanehkaran, Y., Pusatli, T., Chengyong, J., Chen, J., Cemiloglu, A., Azarafza, M., and Derakhshani, R.: Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis, Water, 14, 3743, https://doi.org/10.3390/w14223743, 2022. a
ASTM-D3080: Standard Test Method for Direct Shear Test of Soils Under Consolidated Drained, Tech. rep., ASTM International, https://doi.org/10.1520/D3080_D3080M-23, 2012. a
ASTM-D7181: Standard Test Method for Consolidated Drained Triaxial Compression Test for Soils, Tech. rep., ASTM, https://doi.org/10.1520/D7181, 2011. a
Baecher, G. B.: 2021 Terzaghi Lecture: Geotechnical Systems, Uncertainty, and Risk, J. Geotech. Geoenviron., 149, 03023001, https://doi.org/10.1061/JGGEFK.GTENG-10201, 2023. a, b
Barbosa, J. S. F., Correa-Gomes, L. C., Dominguez, J. M. L., Cruz, S. A. S., and Souza, J. S. D.: Petrografia e litogeoquímica das rochas da parte oeste do alto de Salvador, Bahia, Rev. Bras. Geociências, 35, 09–22, https://doi.org/10.25249/0375-7536.200535S40922, 2005. a, b
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Short summary
Many cities have unplanned areas with irregular occupation, mainly by the poorest parts of the population, who are pushed to live in high-risk areas without public services and subject to the impacts of extreme events. In this paper, emergency calls, reflecting exposed human population behavior, accessibility, and public awareness, are used, along with machine learning, to indicate the seriousness of the situation and the need for abatement measures by the municipal administration.
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