Exploration
Seyedeh Golaleh Hosseini; Kourosh Shahriar; Mohammadamin Karbala
Abstract
Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage ...
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Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage systems at the Angouran lead–zinc mine. In the first phase, the hydro-mechanical behavior of the rock mass was simulated using UDEC software, demonstrating that increased normal stress reduces fracture aperture and permeability. The simulated pore pressure (4.5×10⁵ Pa) closely matched the field measurements (4.4×10⁵ Pa), with only a 2.2% deviation. In the second phase, a multi-criteria decision-making approach using the Analytic Hierarchy Process (AHP) and input from 32 domain experts identified Q4 (very high quality) and Q2 (medium quality) indicators as the most influential criteria. In the third phase, three machine learning models—linear regression, polynomial regression, and artificial neural networks (ANNs)—were trained on piezometric data to predict water discharge. The ANN model outperformed the other models, achieving an R² of 0.94 and RMSE of 0.18, effectively capturing the nonlinear dynamics of groundwater flow within the mine. The findings highlight that the integration of numerical modeling, expert-based decision analysis, and AI-driven prediction provides a robust and innovative approach for designing and managing mine dewatering systems, with potential applicability to other complex hydrogeological environments.
