Application of Machine Learning Models for Slope Instabilities Prediction in Open Cast mines
Keywords:
machine learning, slope failure, 5-fold cross validation, ROC curves.Abstract
Because slope breakdown can result in severe disasters, slope stability analysis and prediction are crucial. The effectiveness of four machine learning techniques for the prediction of slope stability was compared in this paper. Conventional analysis of slope instability methods (e.g., originally developed in the early part of the 20th century) were widely employed as design aids. Many academics are drawn to them because they provide more advanced design tools, such as machine learning-based learning analytics. The current study's major goal is to analyze and optimize several ML-based models for predicting the safety factor. We used multiple ML-based techniques in this study to predict the factor of safety against slope instabilities. For slope stability prediction, four regression approaches were used: Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Multiple Linear Regression (MLR), and Simple Linear Regression (SLR), and. To train and test the four classification techniques, a data set consisting of more than 20 local and international slopes of projects was collected, with essential parameters of the four models tuned using the 5-fold cross validation approach. The four regression algorithms' prediction results were compared and examined. The correctness, Kappa, and receiver operating parametric curve findings show that both of the MLP and MLR models can produce reasonably satisfactory outcomes, with the MLP model outperforming the other three learning approaches.
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