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Article

A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil

1
Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia
2
Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia
3
Verterra Ecological Engineering, Brisbane, QLD 4000, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Miguel Martínez-Graña
Sustainability 2021, 13(20), 11267; https://doi.org/10.3390/su132011267
Received: 14 August 2021 / Revised: 22 September 2021 / Accepted: 27 September 2021 / Published: 13 October 2021
Dispersive spoil/soil management is a major environmental and economic challenge for active coal mines as well as sustainable mine closure across the globe. To explore and design a framework for managing dispersive spoil, considering the complexities as well as data availability, this paper has developed a Bayesian Belief Network (BBN)-a probabilistic predictive framework to support practical and cost-effective decisions for the management of dispersive spoil. This approach enabled incorporation of expert knowledge where data were insufficient for modelling purposes. The performance of the model was validated using field data from actively managed mine sites and found to be consistent in the prediction of soil erosion and ground cover. Agreement between predicted soil erosion probability and field observations was greater than 74%, and greater than 70% for ground cover protection. The model performance was further noticeably improved by calibration of Conditional Probability Tables (CPTs). This demonstrates the value of the BBN modelling approach, whereby the use of currently best-available data can provide a practical result, with the capacity for significant model improvement over time as more (targeted) data come to hand. View Full-Text
Keywords: mine rehabilitation; predictive probabilistic modelling; environmental risk; soil erosion; adaptive decision-making mine rehabilitation; predictive probabilistic modelling; environmental risk; soil erosion; adaptive decision-making
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MDPI and ACS Style

Ghahramani, A.; Bennett, J.M.; Ali, A.; Reardon-Smith, K.; Dale, G.; Roberton, S.D.; Raine, S. A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil. Sustainability 2021, 13, 11267. https://doi.org/10.3390/su132011267

AMA Style

Ghahramani A, Bennett JM, Ali A, Reardon-Smith K, Dale G, Roberton SD, Raine S. A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil. Sustainability. 2021; 13(20):11267. https://doi.org/10.3390/su132011267

Chicago/Turabian Style

Ghahramani, Afshin, John McLean Bennett, Aram Ali, Kathryn Reardon-Smith, Glenn Dale, Stirling D. Roberton, and Steven Raine. 2021. "A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil" Sustainability 13, no. 20: 11267. https://doi.org/10.3390/su132011267

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