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Prophetical Modeling Using Limit Equilibrium Method and Novel Machine Learning Ensemble for Slope Stability Gauging in Kalimpong

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Abstract

Slope stability assessment is necessary to evaluate the safety of natural or man-made slopes. This analysis is crucial for determining the potential risk that could result in landslides or other hazardous situations. This research investigates the landslide predictability of crucial locations in Kalimpong, Darjeeling Himalayas, which are characterized by complicated geology in tough terrains. The study concentrated on the factor of safety determination process for dry and saturated conditions utilizing the GeoStudio commercial software “SLOPE/W” based on limit equilibrium method and provide an analytical comparison using computational intelligence and machine learning approaches. Support vector machine, decision tree, random forest, logistic regression, naïve Bayes, k-nearest neighbors algorithm, and AdaBoost are used as machine learning classifiers having a strong capability of predicting slope failures and perils. Five parameters, namely cohesion, internal friction angle, unit weight, slope angle, and slope height, are chosen as random variables and stability condition as output. Inter-criteria correlation (CRITIC)-based method is utilized to perform sensitivity analysis denoting the greatest impacting parameter, i.e., slope height. Novel ensemble approach R-Boost is identified to give maximum accuracy in comparison to all seven machine learning methods. By multifold cross-validation, R-Boost has the greatest forecasting skill, with an average classification accuracy of 0.725 and in terms of area under the curve, random forest (RF) represents an average value of 0.81, followed by R-Boost at 0.798. Among all predictive models, particularly R-Boost followed by RF provides quite similar results as obtained by SLOPE/W. This technique will be particularly beneficial in preventing, anticipating, and reducing the impact of these sorts of catastrophic disasters, which function as substantial barriers to the nation's socioeconomic progress

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  1. Department of Civil Engineering, Delhi Technological University, Delhi, 110042, India

    Vaishnavi Bansal & Raju Sarkar

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Bansal, V., Sarkar, R. Prophetical Modeling Using Limit Equilibrium Method and Novel Machine Learning Ensemble for Slope Stability Gauging in Kalimpong. Iran J Sci Technol Trans Civ Eng 48, 411–430 (2024). https://doi.org/10.1007/s40996-023-01156-0

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