Surface-strip coal mine rehabilitation risk assessment : the development of an integrated rehabilitation risk assessment model for use in South Africa and Australia

Abstract

Surface-strip coal mine rehabilitation planning in South Africa and Australia is immature. Rehabilitation risk assessment, despite being advocated by leading practice guidelines and in some instances by legislation, is conducted with minimum requirements often met by rehabilitation professionals. Specialist data is gathered during mine approval and for the environmental impact assessment process. However, the focus of this is toward assessing mining impacts and not for rehabilitation risk assessment. Quantitative, integrated, multi-disciplinary rehabilitation risk assessment is seldom undertaken. This thesis provides a methodology towards the development of a quantitative, integrative, multi-disciplinary rehabilitation risk assessment model. Its purpose being to 'profile' surface-strip coal mine sites, in terms of their rehabilitation risk and potential for rehabilitation failure, from the outset of mine operations, with adjustments possible progressively during mine operations. The methodology was developed by first reviewing techniques suitable for the development of the model, as well as techniques developed by others. Bayesian networks (BN) were found to be the most suited. A R2AIN framework was then provided as a process towards developing several BN risk event models that can amalgamate to form a synthesis rehabilitation risk assessment model. A case study soil compaction BN model was used to demonstrate the framework in South Africa and Australia. The case study showed that it is possible to integrate and quantify rehabilitation risk, and most importantly to segregate risk into discrete contributing multidisciplines for analysis. Risk percentages can be calculated per multi-discipline, per mine phase, per site, to aid site risk ‘profiling’. It is recommended that further risk event BN models be prioritised for development and that a rehabilitation risk assessment model be developed to synthesise these into one model. This will require continuous improvements in the method, to build confidence, including extensive risk event and synthesis BN model evaluation and testing; improved BN input node states and values; and simplification of the conditional probability table construction method. Adaptation to other mining types, development activities and other regions should be investigated, as well as spatial linkages to geographic information systems. This research contribution improves upfront mine rehabilitation planning and decision making, providing improved tools and techniques than what currently exist.