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Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for

Bilel Zerouali1, Nadjem Bailek2,3, Aqil Tariq4

  • 1Laboratory of Architecture, Cities and Environment, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali University of Chlef, B.P. 78C, 02180, Ouled Fares, Chlef, Algeria.

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Summary

Machine learning models, particularly generative adversarial networks (GANs), effectively classify slope stability. Combining feature selection with GANs significantly enhances accuracy for critical geotechnical engineering applications.

Keywords:
Feature selectionGeotechnicalNatural hazardSlope stabilitySoil stability

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Area of Science:

  • Geotechnical Engineering
  • Machine Learning
  • Data Science

Background:

  • Slope stability is critical for infrastructure safety and hazard mitigation.
  • Identifying key factors influencing slope stability is essential for accurate assessment.
  • Traditional methods may not fully capture the complexities of slope stability prediction.

Purpose of the Study:

  • To identify the most influential factors affecting slope stability.
  • To evaluate the performance of various machine learning models for slope stability classification.
  • To assess the impact of advanced feature selection techniques on model performance.

Main Methods:

  • Correlation analysis and random forest regressor for feature importance.
  • Evaluation of deep learning models: RNNs, LSTMs, and GANs.
  • Integration of binary bGGO feature selection with GAN models.

Main Results:

  • Cohesion, unit weight, slope height, and friction angle identified as critical factors.
  • The GAN model achieved high accuracy (0.913) and AUC (0.9285).
  • The bGGO-GAN model demonstrated superior performance, reaching 95% accuracy on 627 samples.

Conclusions:

  • Advanced machine learning, especially GANs with feature selection, significantly improves slope stability classification.
  • The bGGO-GAN model offers high accuracy, generalizability, and enhanced predictive values.
  • This approach provides valuable insights for geotechnical engineering and hazard mitigation.