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

Afshin Ghahramani,Aram Ali,Kathryn Reardon-Smith,Steven Raine,Stirling D Roberton,Glenn Dale,John Mclean Bennett

doi:10.3390/su132011267

Afshin Ghahramani, Aram Ali + Show 5 more

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https://doi.org/10.3390/su132011267

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Journal: Sustainability	Publication Date: Oct 13, 2021
Citations: 2	License type: CC BY 4.0

Affiliation: University of Southern Queensland

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.

Highlights

Dispersive spoil/soil management is a significant environmental and economic issue in parts of Australia and internationally [1]
To investigate and better understand the interconnected nature of dispersive spoil management, we developed a conceptual model using a graphical Bayesian Belief Network (BBN) framework to capture the range of interacting variables in dispersive spoil management systems, including spoil physical and chemical characteristics; spoil treatment regimes; site characteristics; site management practices including revegetation and hard engineering works; and the process-based interactions between these [5,6,17]
Limited data to train and test a BBN model may restrict the applicability of the model as a decision support tool; as we show here, a well-developed model with enough expert knowledge may overcome this constraint

Summary

Introduction

Dispersive spoil/soil management is a significant environmental and economic issue in parts of Australia and internationally [1]. In Australia, the management of dispersive mine spoil is significant for the coal mining industry, where spoil materials frequently derive from dispersive pedological and geological profiles. This presents challenges for the management of spoil in active mines, sustainable mine closure [2], and the rehabilitation/management of historic or abandoned mine sites. In the Bowen region of the state of Queensland, Australia, the instantaneous liability for rehabilitating dispersive spoil dumps is estimated at $AU2 to 3 billion. While the causes of dispersion are relatively well understood, functional integration of the processes that lead to failure of dispersive mine spoil, the interaction of erosion processes, and the physical and chemical properties of dispersive materials, remains incomplete

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