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

Author

Listed:
  • Afshin Ghahramani

    (Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • John McLean Bennett

    (Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • Aram Ali

    (Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • Kathryn Reardon-Smith

    (Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • Glenn Dale

    (Verterra Ecological Engineering, Brisbane, QLD 4000, Australia)

  • Stirling D. Roberton

    (Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • Steven Raine

    (Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

Abstract

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.

Suggested Citation

  • Afshin Ghahramani & John McLean Bennett & Aram Ali & Kathryn Reardon-Smith & Glenn Dale & Stirling D. Roberton & Steven Raine, 2021. "A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11267-:d:654848
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    References listed on IDEAS

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    1. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    2. Andrea Koch & Alex McBratney & Mark Adams & Damien Field & Robert Hill & John Crawford & Budiman Minasny & Rattan Lal & Lynette Abbott & Anthony O'Donnell & Denis Angers & Jeffrey Baldock & Edward Bar, 2013. "Soil Security: Solving the Global Soil Crisis," Global Policy, London School of Economics and Political Science, vol. 4(4), pages 434-441, November.
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    4. Dang, A. & Bennett, J. McL. & Marchuk, A. & Biggs, A. & Raine, S.R., 2018. "Quantifying the aggregation-dispersion boundary condition in terms of saturated hydraulic conductivity reduction and the threshold electrolyte concentration," Agricultural Water Management, Elsevier, vol. 203(C), pages 172-178.
    5. Kleemann, Janina & Celio, Enrico & Fürst, Christine, 2017. "Validation approaches of an expert-based Bayesian Belief Network in Northern Ghana, West Africa," Ecological Modelling, Elsevier, vol. 365(C), pages 10-29.
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