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<p>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 three different models, the Random Forest (RF), Artificial Neural Network (ANN) and Scoring Sheet (SC) and the performance of the models were compared. 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, presenting AUC above 0.800 but the ANN model exhibited the most consistent results, demonstrating a higher precision (0.815) and enhanced accuracy (0.814) in its classification against 0.797 and 0.799 of precision and 0.797 and 0.788 of accuracy in the RF and SC models respectively. The analysis revealed that slope gradient was a key factor in determining susceptibility in the three models performed, with Feature Importance of 0.255 in the Artificial Neural Network (ANN) model, 0.285 in the Random Forest (RF) model, and 0.293 in the Scoring Sheet (SC) model. The analysis showed that high slope areas were 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 Machine Learning models demonstrated high accuracy in determining the spatial distribution of susceptibility, providing a faster and more accurate analysis.</p>

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

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Lucas Krein Rademann,
  • Lucas da Silva Ribeiro,
  • Romario Trentin,
  • Luís Eduardo de Souza Robaina

摘要

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 three different models, the Random Forest (RF), Artificial Neural Network (ANN) and Scoring Sheet (SC) and the performance of the models were compared. 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, presenting AUC above 0.800 but the ANN model exhibited the most consistent results, demonstrating a higher precision (0.815) and enhanced accuracy (0.814) in its classification against 0.797 and 0.799 of precision and 0.797 and 0.788 of accuracy in the RF and SC models respectively. The analysis revealed that slope gradient was a key factor in determining susceptibility in the three models performed, with Feature Importance of 0.255 in the Artificial Neural Network (ANN) model, 0.285 in the Random Forest (RF) model, and 0.293 in the Scoring Sheet (SC) model. The analysis showed that high slope areas were 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 Machine Learning models demonstrated high accuracy in determining the spatial distribution of susceptibility, providing a faster and more accurate analysis.