EGU22-4250
https://doi.org/10.5194/egusphere-egu22-4250
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Landslide Susceptibility Modeling of an Escarpment in Southern Brazil using Artificial Neural Networks as a Baseline for Modeling Triggering Rainfall

Luísa Vieira Lucchese1,2, Guilherme Garcia de Oliveira3, Alexander Brenning2, and Olavo Correa Pedrollo1
Luísa Vieira Lucchese et al.
  • 1Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil (luisa.lucchese@ufrgs.br)
  • 2Institut für Geographie, Friedrich-Schiller-Universität Jena, Jena, Germany
  • 3Departamento Interdisciplinar, Universidade Federal do Rio Grande do Sul, Tramandaí, Brazil

Landslide Susceptibility Mapping (LSM) and rainfall thresholds are well-documented tools used to model the occurrence of rainfall-induced landslides. In the case of locations where only rainfall can be considered a main landslide trigger, both methodologies apply essentially to the same locations, and a model that encompasses both would be an important step towards a better understanding and prediction of landslide-triggering rainfall events. In this research, we employ spatially cross-validated, hyperparameter tuned Artificial Neural Networks (ANNs) to predict the susceptibility to landslides of an area in southern Brazil. In a next step, we plan to add the triggering rainfall to this Artificial Intelligence model, which will concurrently model the susceptibility and the triggering rainfall event for a given area. The ANN is of type Multi-Layer Perceptron with three layers. The number of neurons in the hidden layer was tuned separately for each cross-validation fold, using a method described in previous work. The study area is the escarpment in the limits of the municipalities of Presidente Getúlio, Rio do Sul, and Ibirama, in southern Brazil. For this area, 82 landslides scars related to the event of December 17th, 2020, were mapped. The metrics for each fold are presented and the final susceptibility map for the area is shown and analyzed. The evaluation metrics attained are satisfactory and the resulting susceptibility map highlights the escarpment areas as most susceptible to landslides. The ANN-based susceptibility mapping in the area is considered successful and seen as a baseline for identifying rainfall thresholds in susceptible areas, which will be accomplished with a combined susceptibility and rainfall model in our future work.

How to cite: Vieira Lucchese, L., Garcia de Oliveira, G., Brenning, A., and Correa Pedrollo, O.: Landslide Susceptibility Modeling of an Escarpment in Southern Brazil using Artificial Neural Networks as a Baseline for Modeling Triggering Rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4250, https://doi.org/10.5194/egusphere-egu22-4250, 2022.

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