*5.3. Risk-Based Analysis*

The risk severity impacts are presented in Table 6, reflecting the classification of risks and the units/parameters used to measure the risk severity. In turn, the risk severity is classified into three main categories: low, medium and high, along with the numerical values. Based on these values, the severity of any impact can be determined (the risk impact measuring units were identified by empirical evidence and by considering the specific geo-environmental conditions of the mine restoration plan that were described in the Section 5.1). These values are based on the PMI [50] practice, not to follow a linear order for the escalation of risk severity, but to apply the relation a(n) <sup>=</sup> 2n (n∈N0). Thus: 2<sup>0</sup> <sup>=</sup> 1: low impact, 2<sup>1</sup> = 2: medium impact and 22 = 4: high impact [51] (Table 7).


**Table 6.** Classification of Risk Severity Impacts.


**Table 6.** *Cont.*

**Table 7.** Risk-Based Analysis.



**Table 7.** *Cont.*

#### *5.4. Risk Factors Definition*

The definition of the relative weight of each criterion has been performed by applying the AHP technique according to the following steps [32,37,38,45]:



1: Equal importance





**Table 10.** Normalized Matrix and Priority Vector, *WRi* (Risk Weights).

#### *5.5. Ranking of Alternatives*

The application of TOPSIS aims to define the score of each alternative and the final ranking of the alternatives and, hence, to demonstrate the lower risk, or 'best', alternative restoration method (Table 7). The steps developed for TOPSIS technique application are the following:


The final ranking is: C3\* = 0.819 < C2\* = 0.515 < C1\* = 0.447 (Table 11). The lower risk alternative with the higher performance in the scheme AHP/TOPSIS is A3, which refers to the combined restoration technology. The second best is A2 (restoration focused on natural processes) and the last in the sequence, the higher risk alternative, is A1 (technical restoration). This result clearly indicates that the evaluators (experts) consider the alternative A3 as optimal, taking into account that this has the lower total risk score and also that it is based on the balance between the technical activities and the natural processes at the mined-out area.



**Table 11.** *Cont.*




#### *5.6. Discussion*

The analysis confirms that the suggested methodology can provide a decision making framework for the landscape planning and restoration of surface mining projects. In this context, the proposed approach (a) ensures objectivity in the identification and determination of the relative weight of each risk factor/sub-factor and (b) demonstrates the selection of the optimum restoration technology in a clear and explicit way, enabling decision makers to understand the results of the multi-criteria evaluation process and to make a reasonable decision.

The lack of precise quantitative data in the mining projects makes the complete understanding of the restoration needs and perspectives quite difficult. To overcome this deficiency, the study suggests the knowledge elicitation of mining managers and production engineers and the transformation in an explicit form by combining AHP and TOPSIS techniques.

The combination of the two techniques allows better decision making, considering the advantages and the shortcomings of each technique. For example, since TOPSIS by its nature (based on the Euclidean distance) does not allow correlation of the criteria, the correlation can be achieved by means of the AHP pairwise comparison.

Regarding the strengths of the AHP/TOPSIS combination, the AHP is flexible in application and allows the consideration of subjective and objective factors in the decision making process. Furthermore, in AHP applications, the experts, through interpersonal contacts or properly prepared questionnaires, offer their judgment for the structuring and processing of reciprocal matrices, where the risk factors are identified and correlated. The higher experience of the experts in the management of mining restoration risks ensures the better identification and evaluation of the relative weights of risk factors and sub-factors. In this work, the option of interviewing three qualified experts with long term experience (>20 years) in surface mining systems was adopted. In this way, the relative weights of the identified risks meet a high level of technical justification and reliability that increases the objectivity of the technique, to the extent possible. In addition, the application of TOPSIS allows the "bounding" of the risk ranking between the "positive ideal" and the "negative ideal" solution, thus, the definition of the total risk is combined with mathematical clarity and simplicity.

On the other hand, with the combination of the AHP and TOPSIS techniques, the subjectivity cannot be eliminated, since the experts have their own perceptions and biases on the significance of each individual risk in relation to the restoration alternatives. This problem is more complicated in cases where the group of experts is not exclusively composed of mining specialists, but also includes representatives assigned by municipalities, public agencies, NGOs, etc., with different educational background, environmental perception or objectives. In such cases, many repetitions of the AHP may be required to fulfill the consistency criterion. Another weakness is the performance limitation and inefficiency, especially in cases where the number of alternatives and/or the number of risk factors and sub-factors is high.

In conclusion, for the successful implementation of the methodology, an appropriate organization and control of the whole decision making context is required. The objectives of the restoration project should be well defined, and the weaknesses and inefficiencies of the AHP/TOPSIS applications must be identified and considered.

#### **6. Conclusions and Perspectives for Further Research**

The MCDM philosophy could be adopted to support the risk management needs in projects of surface lignite mines restoration. The selection of a lower risk restoration technology/method is critical as it provides a substantial basis for the decision makers to realize that a restoration project is technically feasible and environmentally friendly, with benefits for the environment, society and economy, and can be completed meeting the cost and time constraints.

This work makes an important contribution to the field of geo-energy, by demonstrating how the principles of risk management can be adapted in complex projects of mining restoration on the basis of computational simplicity of the applied MCDM methods. In this context, the appropriate

multi-criteria methodology can be an efficient decision making support tool in terms of objectivity, mathematical consistency and clarity of the quantitative results.

The suggested methodology enables the evaluation and selection of technically appropriate and low risk technologies/methods for the restoration/reclamation of closing surface mines through the combination of AHP and TOPSIS techniques, with an aim to solve practical risk-based decision making problems. However, the combined AHP and TOPSIS application has specific limitations, especially in cases where the number of criteria or the alternatives is high, or the group of experts presents educational, professional or cultural heterogeneities.

The AHP method, as a decision-making tool, is simple and easy to use and allows the low cost analysis and quantification of project risks, by aggregating the knowledge of mining and restoration experts and transforming it in an explicit form. The TOPSIS uses the relative weights of the risk-based criteria and sub-criteria, identified by the AHP, as inputs of the numerical calculations for the definition of the ideal and negatively ideal solutions and, therefore, for obtaining the ranking of mine restoration alternatives. Both techniques are used, on an individual basis or in combination with others, in the evaluation of mining projects.

Finally, some proposals for further research are pointed out. One relates to the investigation of restoration risks in a more analytical concept. For example, the crisp values 1, 2 and 4 corresponding to risk impacts classification as low, medium and high, could be further analyzed in five levels, by defining interim numerical values between low and medium and also between medium and high levels of severity. Thus, the classification of risk impacts can be analyzed in a more detailed basis. Moreover, the aforementioned crisp values can be interpreted in a form of fuzzy variables to express the uncertainty of each specific risk using simplified linguistic expressions. Therefore, the methodology could be further improved in more sophisticated approaches. Moreover, other MCDM techniques such as PROMETHEE (I or II), ELECTRE, SMART, VIKOR, DEMATEL, fuzzy versions of these techniques, Bayesian networks, neuro-fuzzy algorithms or other hybrid techniques could be used instead of TOPSIS for risks quantification and processing. Finally, a comparative analysis of the performance and efficiency of various MCDM methods in a decision making framework related to mine restorations can be performed.

**Author Contributions:** Conceptualization, P.-M.S., C.R. and F.P.; methodology, P.-M.S., C.R. and F.P.; validation, P.-M.S., C.R. and F.P.; formal analysis, P.-M.S., C.R. and F.P.; data curation, P.-M.S., C.R. and F.P.; writing—original draft preparation, P.-M.S., C.R. and F.P.; writing—review and editing, P.-M.S., C.R. and F.P.; visualization, P.-M.S., C.R. and F.P.; supervision, P.-M.S., C.R. and F.P.; project administration, P.-M.S., C.R. and F.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank Professor Zach Agioutantis, Department of Mining Engineering, University of Kentucky, for his constructive comments on this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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