Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone.

Rainfall and its interaction with soil, rock, and environmental factors such as soil moisture content, temperature variations, groundwater levels, and vegetation cover are critical determinants of slope stability in geotechnical engineering. This study introduces an innovative AI-driven system designed to predict the geotech- nical properties of slopes post-monsoon season. Utilizing a comprehensive dataset collected before and after the monsoon, the system targets the prediction of es- sential properties including unit weight, cohesion, and friction angle-parameters significantly influenced by monsoon rains. To quantify these impacts, the system calculates the percentage changes in these properties. A robust Exploratory Data Analysis (EDA) was conducted to elucidate the distributions of pre-monsoon and post-monsoon properties and uncover interrela- tionships among them. Machine learning models, encompassing Linear Regression, Random Forest Regression, Gradient Boosting Regressors, Support Vector Regres- sors, and Ensemble Models, were employed to predict post-monsoon properties based on pre-monsoon data and calculated changes. The models' accuracies were evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R), Mean Bias Deviation (MBD), and Willmott's Index of Agreement (d). Furthermore, the study explores transforming the regression task into a binary classification problem, categorizing slopes as stable or unstable based on predicted post-monsoon properties. Classification performance was assessed using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC) metric. Feature importance and sensitivity analyses were performed using SHAP (SHapley Additive exPlanations) to identify the most influential factors affecting slope stability predictions. To enhance model robustness and generalizability, synthetic data was generated using Generative Adversarial Networks (GANs), augmenting the original dataset and ensuring a diverse range of conditions. The AI system demonstrated excep- tional capabilities in accurately predicting geotechnical property changes, thereby enabling engineers to proactively manage risks and improve slope stability assess- ments. This project underscores the significant potential of integrating advanced machine learning techniques with traditional geotechnical practices, particularly in regions experiencing heavy rainfall, to foster safer and more efficient engineering solutions.