Assessing Slope Stability Reliability through Visual Exploratory Data Analysis and Machine Learning in Kumaon Region of Uttarakhand in India

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 1

Year of Publication : 2025

Authors : Pratul Raj, Lal Bahadur Roy

10.14445/23488352/IJCE-V12I1P106

How to Cite?

Pratul Raj, Lal Bahadur Roy, "Assessing Slope Stability Reliability through Visual Exploratory Data Analysis and Machine Learning in Kumaon Region of Uttarakhand in India," SSRG International Journal of Civil Engineering, vol. 12,  no. 1, pp. 46-66, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I1P106

Abstract:

Slope stability is essential when planning or building any structure or formation on a soil slope. Accurate slope stability analysis involves considering variability in the properties of soil. Several methods are available within these probabilistic frameworks for determining a slope’s safety factor (F.S). Increasing the reliability and accuracy of F.S value calculations can enhance both the stability analysis and stabilization procedures. Researchers have used computational intelligence approaches to obtain high-precision values of F.S. This paper focuses on F.S estimation using various machine learning and computational intelligence methods for comparison. It used six soft computing techniques, namely: Decision Tree (DT), Linear Regression (LR), Support Vector Regression (SVR), Neural Network (NN), k-Nearest Neighbor (KNN), and Random Forest (RF). To improve prediction accuracy, these models accounted for variability in critical soil properties such as slope angle, internal friction, unit weight, cohesion, and slope height. The models were trained with data from field cases, with the safety factor being the output variable. Validations were done using Morgenstern-Price (MP) LEM and Geo-Studio 2018 software. Model performance was carried out in terms of the metrics developed, such as R², RMSE, MAE, MSE, and VAF. The LR model resulted in R² = 0.9354, RMSE = 0.0911, MAE = 0.0703, MSE = 0.0083, and VAF = 93.62%. The graphical analyses applied were ROC curves, actual-versus-predicted plots, and residual graphs, all of which showed that the LR model was effective.

Keywords:

Machine learning, Slope stability, Factor of safety, Limit equilibrium methods, Predictive model.

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