Exploring topographic variables in gully erosion susceptibility mapping in Central Brazil

Explorando variables topográficas en el mapeo de susceptibilidad a la erosión en cárcavas en el centro de Brasil

Laudier Lopes Abreu1, Alessandra Cristina Pereira1, Fabio Corrêa Alves2, Max Well de Oliveira Rabelo1, Elizon Dias Nunes3, Édipo Henrique Cremon1,4,*

1 Instituto Federal de Goiás, Campus Goiânia. Rua 75, 46 Centro, 74055-110, Goiânia, Brazil.

2 Universidade Federal do Oeste da Bahia, Centro das Humanidades. Rua da Prainha 1326, Morada Nobre, CEP 47810-047, Barreiras, Brazil.

3 Universidade Federal de Goiás, Instituto de Estudos Socioambientais. Av. Esperança, s/n Samambaia, 74001-970, Goiânia, Brazil.

4 Instituto Nacional de Pesquisas Espaciais, Divisão de Observação da Terra e Geoinformatica. Avenida dos Astronautas, 1.758, 12227-010, São José dos Campos, Brazil.

* Corresponding author: (E.H. Cremon) This email address is being protected from spambots. You need JavaScript enabled to view it. 

How to cite this article:

Abreu, L.L., Pereira, A.C., Alves, F.C., Oliveira-Rabelo de, M., Dias-Nunes, E., Cremon, E.H., 2025, Exploring topographic variables in gully erosion susceptibility mapping in Central Brazil: Boletín de la Sociedad Geológica Mexicana, 77(3), A121025. http://dx.doi.org/10.18268/BSGM2025v77n3a121025 

Manuscript received: December 6, 2024. Corrected manuscript received: May 21, 2025. Manuscript accepted: August 1, 2025.

ABSTRACT

This study investigates gully erosion susceptibility in southwestern Goiás State, Central Brazil, where extensive gully erosion affects the landscape. The primary objective was to evaluate the predictive power of less commonly analyzed topographic variables, derived from Digital Elevation Models, in comparison with traditional predictors. A total of 5 660 gully samples were mapped, with gully heads selected as the main modeling target due to their geomorphological relevance. The methodology integrated topographic, hydrological, lithological, pedological, and anthropogenic factors. Advanced variable selection methods, including the Recursive Feature Elimination (RFE) algorithm and Variance Inflation Factor (VIF), were employed to enhance model accuracy and eliminate multicollinearity. The Random Forest algorithm was implemented for modeling, calibrated through cross-validation and tested independently using the Area Under the Curve (AUC) metric. Results demonstrated that incorporating unconventional topographic variables, such as multiscale roughness and terrain surface texture, significantly improved predictive performance. The RFE-based model achieved the highest AUC value (0.9459), underscoring the effectiveness of this approach in identifying critical predictors. Gully erosion susceptibility mapping was primarily associated with land use, drainage density, and specific terrain characteristics. The findings emphasize the value of integrating advanced geomorphometric analyses with machine learning to better understand and predict gully erosion processes, such as gullies in southwestern Goiás, Central Brazil.

Keywords: soil erosion modeling, digital elevation models, machine learning applications.