This study aims at developing a machine learning based classification and regression-based models for slope stability analysis. 1140 different cases have been analysed using the Morgenstern price method in GeoSlope for non-homogeneous cohesive slopes as input for classification and regression-based models. Slope failures presents a serious challenge across many countries of the world. Understanding the various factors responsible for slope failure is very crucial in mitigating this problem. Therefore, different parameters which may be responsible for failure of slope are considered in this study. 9 different parameters (cohesion, specific gravity, slope angle, thickness of layers, internal angle of friction, saturation condition, wind and rain, blasting conditions and cloud burst conditions) have been identified for the purpose of this study including internal, external and factors representing the geometry of the slope has been included. Four different classification algorithms namely Random Forest, logistic regression, Support Vector Machine (SVM), and K Nearest Neighbor (KNN) has been modelled and their performances have been evaluated on several performance metrics. A similar comparison based on performance indices has been made among three different regression models Decision tree, random forest, and XGBoost regression.

This study rigorously examines the pressing issue of dump slope stability in Indian opencast coal mines, a problem that has led to significant safety incidents and operational hindrances. Employing machine learning algorithms such as Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Gaussian Naive Bayes (GNB), the study aims to achieve a scientific goal of predictive accuracy for slope stability under various environmental and operational conditions. Promising accuracies were attained, notably with RF (0.98), SVM (0.98), and DT (0.97). To address the class imbalance issue, the Synthetic Minority Oversampling Technique (SMOTE) was implemented, resulting in improved model performance. Furthermore, this study introduced a novel feature importance technique to identify critical factors affecting dump slope stability, offering new insights into the mechanisms leading to slope failures. These findings have significant implications for enhancing safety measures and operational efficiency in opencast mines, not only in India but potentially globally.