An Integrated Decision Support System for Low-Disturbance Surface Mining

Abstract

Low-disturbance mining in surface mining (LDM) can transform traditional surface mine production systems into a more sustainable model by reducing the disturbance of surface mining, minimizing pollutant emissions, and reducing ecological impacts. The purpose of this paper is to explore the LDM evaluation method by applying multi-criteria decision-making to provide technical support for LDM implementation. Therefore, an evaluation method based on the combination of the fuzzy analytical hierarchy process (F-AHP) and grey clustering was proposed. Analyzed in terms of the current status of the evaluation indicators (reality) and the significance of the development of the LDM (desirability). Determined the weights and low-disturbance (LD) levels of the evaluation indicators. Combined with the fuzzy technique for order preference by similarity to an ideal solution (F-TOPSIS), the low-disturbance open pit mining paths are ranked, and finally, the decision support system for low-disturbance mining in surface mining is constructed. This study not only enriches the existing literature on related technologies but also lays the foundation for further research on LDM and provides exploratory insights for deeper improvement of LD level in surface mining.