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Comparative Analysis of Slope Stability for Kalimpong Region under Dynamic Loading Using Limit Equilibrium Method and Machine Benchmark Learning Classifiers

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Abstract

Significant slope destabilisation may become more likely due to the speed at which urbanisation is occurring, as well as the growing necessity for geoengineering initiatives or the growth of the road network. Slope stability analysis is done to lower the risk of landslides and slope failures. The study area, Kalimpong, is well-known for its lush greenery and stunning views and is situated in the Eastern Himalayas. However, it also constantly confronts the risk of landslides because of its rugged topography, potential seismic zone, and heavy monsoon rains. In this study, the results of the factor of safety computed by limit equilibrium (conventional) method have been compared analytically using computational intelligence and machine learning methodologies for both dry and saturated conditions under dynamic loading. Conventional machine learning techniques are combined with seven prediction models. The following algorithms have been chosen for slope stability analysis: support vector machine, k-nearest neighbours, decision tree, random forest, logistic regression, AdaBoost, and gradient boosting. Random cross-validation is used to assess each model's dependability. The stability condition is the result of the random selection of seven parameters: cohesiveness, unit weight, slope height, angle of the slope, internal friction angle, horizontal and vertical pseudo-static coefficient. Moreover, the coefficient of variation method is employed to assess the importance of every indicator in forecasting slope stability. As per the sensitivity analysis, slope stability is primarily affected by cohesiveness. With an average classification accuracy of 0.878, ensembling approach SVM-Boost demonstrates the best prediction abilities among the models tested using multifold cross-validation. The accuracy ratings of SVM and AdaBoost were 0.865 and 0.834, respectively. When combined with SLOPE/W advances, novel SVM-Boost exhibits the highest exactitude, hegemony, and best outcomes in slope stability prediction. Future earthquakes, strong rainfall, and human activity could cause the slope to collapse. The outcome demonstrates machine learning's enormous potential for enhancing slope stability assessments and provides a means of raising the effectiveness and safety of slope management.

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    Vaishnavi Bansal & Raju Sarkar

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Bansal, V., Sarkar, R. Comparative Analysis of Slope Stability for Kalimpong Region under Dynamic Loading Using Limit Equilibrium Method and Machine Benchmark Learning Classifiers. Iran J Sci Technol Trans Civ Eng (2024). https://doi.org/10.1007/s40996-024-01344-6

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