Evaluation of performance of different machine learning techniques for mapping landslide susceptibility associated with extreme rainfall events in southern Brazil

Landslides represent a primary geological process that triggers hazards in steep slope areas, affecting infrastructure and sometimes causing loss of life. Susceptibility mapping is a critical component in the mitigation of landslide-induced disasters, providing technical expertise to support public policy decisions. In May 2024, a significant rainfall event occurred in Southern Brazil, leading to multiple landslides and the transgression of previously established limits of slope stability. Hence, it became necessary to study the landslide susceptibility of this region. Given the complex nature of the landslide process, machine learning tools were used to map the landslide susceptibility using Random Forest (RF), Artificial Neural Network (ANN) and Scoring Sheet (SC) models to compare the performance of these models. The geo-environmental parameters of slope, elevation, slope orientation, catchment area, and curvature were used to train the models. All three models were effective in mapping susceptibility, but the ANN model exhibited the most consistent results, demonstrating a higher frequency of true positives and enhanced accuracy in its classification. The analysis revealed that slope gradient was a key factor in determining susceptibility, with high slope areas being more susceptible, particularly on northeast and east-facing slopes. The data analyzed in this study refers to an extreme rainfall event where the geomorphic thresholds are different from the standards expected for landslide occurrence, making it difficult to determine susceptibility using traditional methods. However, the ML models demonstrated high accuracy in determining the spatial distribution of susceptibility, providing a faster and more accurate analysis.