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<p>This research investigates the application of ensemble-based computational paradigms to estimate the stability of Mount St. Helens. Scoops3D was initially utilized for conducting slope stability investigations, followed by computational modeling of the factor of safety (FOS) employing various influencing parameters. Four base models including, AdaBoost regressor, decision tree regressor, extra tree regressor, and gradient boosting regressor, and a bagging-based ensemble learning (BG-ENSM) framework, were used for this purpose. The influence of pore-pressure ratio (<i>r</i><sub>u</sub>) on the stability of Mount St. Helens was examined under both seismic and non-seismic conditions across three distinct scenarios (i.e., Cases 1, 2, and 3) with <i>r</i><sub>u</sub> values of 0, 0.3, and both 0 and 0.3. For model construction and validation, a total of 500, 500, and 1000 samples were examined for the <i>r</i><sub>u</sub> cases 1, 2, and 3, respectively. Following computational modeling, the results of the applied paradigms were assessed using several indices. Experimental outcomes exhibit that the proposed BG-ENSM framework achieved the most desired estimation of FOS with <i>R</i><sup>2</sup> of 0.9968, 0.9959, and 0.9985 against Cases 1, 2, and 3, respectively. Based on the overall results and the outcomes of parametric analysis and Monte Carlo simulation, the employed BG-ENSM framework can be considered as a viable tool for stability estimation of Mount St. Helens considering the effect of <i>r</i><sub>u</sub> in seismic and non-seismic conditions interaction of these two parameters.</p>

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Post-Catastrophic Stability Assessment of Mount St. Helens Using an Efficient Bagging-Based Ensemble Learning Paradigm

  • 期刊论文

  • 发表日期：

    2025年10月16日

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  • Sumit Kumar,
  • Shashank Shekhar Kamal,
  • Subodh Kumar Suman,
  • Avinash Kumar,
  • Abidhan Bardhan

摘要

This research investigates the application of ensemble-based computational paradigms to estimate the stability of Mount St. Helens. Scoops3D was initially utilized for conducting slope stability investigations, followed by computational modeling of the factor of safety (FOS) employing various influencing parameters. Four base models including, AdaBoost regressor, decision tree regressor, extra tree regressor, and gradient boosting regressor, and a bagging-based ensemble learning (BG-ENSM) framework, were used for this purpose. The influence of pore-pressure ratio (ru) on the stability of Mount St. Helens was examined under both seismic and non-seismic conditions across three distinct scenarios (i.e., Cases 1, 2, and 3) with ru values of 0, 0.3, and both 0 and 0.3. For model construction and validation, a total of 500, 500, and 1000 samples were examined for the ru cases 1, 2, and 3, respectively. Following computational modeling, the results of the applied paradigms were assessed using several indices. Experimental outcomes exhibit that the proposed BG-ENSM framework achieved the most desired estimation of FOS with R2 of 0.9968, 0.9959, and 0.9985 against Cases 1, 2, and 3, respectively. Based on the overall results and the outcomes of parametric analysis and Monte Carlo simulation, the employed BG-ENSM framework can be considered as a viable tool for stability estimation of Mount St. Helens considering the effect of ru in seismic and non-seismic conditions interaction of these two parameters.