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Updated: May 26, 2025

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
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Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations.

Kennedy C Onyelowe1,2, Ahmed M Ebid3, Shadi Hanandeh4

  • 1Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, 440109, Nigeria. kennedychibuzor@kiu.ac.ug.

Scientific Reports
|February 23, 2025
PubMed
Summary

This study enhances slope stability analysis using soft computing, with artificial neural networks (ANN) outperforming other models in predicting the factor of safety (FOS). Group Method of Data Handling (GMDH) offers a practical advantage with its closed-form equation for manual application.

Keywords:
ANNAdvanced machine learningFactor of safetyGeohazardGeophysical flowSlope stability

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Area of Science:

  • Geotechnical Engineering
  • Computational Intelligence
  • Machine Learning Applications

Background:

  • Slope stability analysis is crucial in geotechnical engineering but challenged by soil variability and costly on-site testing.
  • Soft computing offers a practical alternative for simulating slope stability, reducing the need for extensive fieldwork.
  • Previous analyses were hindered by unrealistic data entries and a lack of optimized input parameters.

Purpose of the Study:

  • To investigate the predictive capabilities of Class Noise Two (CN2), Stochastic Gradient Descent (SGD), Group Method of Data Handling (GMDH), and artificial neural network (ANN) for slope safety factor (FOS) prediction.
  • To refine slope stability analysis by curating literature data and utilizing dimensionless input parameters.
  • To identify the most effective soft computing model for accurate and practical slope stability assessment.

Main Methods:

  • Literature data on slope stability was collected, curated, and sorted, reducing 349 entries to 296 realistic data points.
  • Input variables were transformed into three dimensionless parameters: C/γ.h, tan(ϕ)/tan(β), and ρ/γ.h.
  • The performance of CN2, SGD, GMDH, and ANN models was evaluated using metrics like Sum of Squared Errors (SSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²).

Main Results:

  • Artificial Neural Network (ANN) demonstrated superior performance, achieving an R² of 0.946, SSE of 62%, MAE of 0.27, and MSE of 0.21.
  • Group Method of Data Handling (GMDH) ranked second and uniquely provides a closed-form equation for manual slope stability design.
  • The refined models significantly outperformed previous work due to data cleaning, dimensionless parameterization, and advanced machine learning techniques.

Conclusions:

  • ANN is the most effective intelligent model for predicting slope safety factors based on the evaluated metrics.
  • GMDH offers a valuable alternative due to its ability to generate a closed-form equation, facilitating manual application in engineering design.
  • Data curation, dimensionless parameterization, and the selection of appropriate machine learning algorithms are critical for improving slope stability analysis accuracy.