Quantifying Rehabilitation Risks for Surface-strip Coal Mines Using a Soil Compaction Bayesian Network in South Africa and Australia: To Demonstrate the R 2 AIN™ Framework.

Environmental information is acquired and assessed during the environmental impact assessment process for surface-strip coal mine approval. However, integrating these data and quantifying rehabilitation risk using a holistic multi-disciplinary approach is seldom undertaken. We present a rehabilitation risk assessment integrated network (R2 AIN™) framework, that can be applied using Bayesian networks (BNs), to integrate and quantify such rehabilitation risks. Our framework has seven steps, including key integration of rehabilitation risk sources and the quantification of undesired rehabilitation risk events to the final application of mitigation. We demonstrate the framework using a soil compaction Bayesian network (BN) case study in the Witbank Coalfield, South Africa and the Bowen Basin, Australia. Our approach allows for a probabilistic assessment of rehabilitation risk associated with multi-disciplines to be integrated and quantified. Using this method, a site's rehabilitation risk profile can be determined before mining activities commence and the effects of manipulating management actions during later mine phases to reduce risk, can be gauged, to aid decision making. This article is protected by copyright. All rights reserved.