Prediction of Homogeneous Earthen Slope Safety Factors Using the Forest and Tree Based Modelling

This study assesses the potential of soft-computing based models i.e. Random Forest ( RF ), Random Tree ( RT ), M5P, Bagging M5P and Stochastic M5P for predicting safety factors ( FS ) of homogenous earthen slopes. For this purpose, a homogenous earthen slope was simulated with Slope/W software that...

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Bibliographic Details
Published in:Geotechnical and geological engineering 2021-04, Vol.39 (4), p.2849-2862
Main Authors: Nouri, Meysam, Sihag, Parveen, Salmasi, Farzin, Abraham, John
Format: Article
Language:English
Subjects:
Civil Engineering
Decision trees
Earth and Environmental Science
Earth Sciences
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematical models
Model accuracy
Model testing
Original Paper
Parameters
Performance evaluation
Root-mean-square errors
Safety factors
Sensitivity analysis
Soil stability
Stability analysis
Stochasticity
Terrestrial Pollution
Waste Management/Waste Technology
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Summary:This study assesses the potential of soft-computing based models i.e. Random Forest ( RF ), Random Tree ( RT ), M5P, Bagging M5P and Stochastic M5P for predicting safety factors ( FS ) of homogenous earthen slopes. For this purpose, a homogenous earthen slope was simulated with Slope/W software that uses the limit equilibrium method ( LEM ). Validation of the method was performed by comparing the calculations with accepted graphical results. For the model performance evaluation, five different statistical parameters including the coefficient of correlation ( CC ), root mean square error ( RMSE ), mean absolute error ( MAE ), scattering index ( SI ) and Nash–Sutcliffe model efficiency coefficient ( NS ) were used. Results showed the stochastic M5P based model performing better than other models with CC = 0.9950, RMSE = 0.0716, MAE = 0.0522, SI = 0.0405 and NS = s 0.9894 for the testing stages. The accuracy of the best performing model was confirmed by comparison with reported real FS and common methods. Another important conclusion was that Hybrid M5P-based models work better than traditional M5P-based models for predicting FS of soil slope. Results of a sensitivity analysis suggest that stability number ( m ) is the most influencing parameter for predicting the FS .
ISSN:0960-3182
1573-1529
DOI:10.1007/s10706-020-01659-x