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dc.contributor.authorBezuidenhout, J.J.
dc.contributor.authorLiebenberg, D.
dc.contributor.authorClaassens, S.
dc.contributor.authorVan Rensburg, L.
dc.date.accessioned2015-05-19T10:50:50Z
dc.date.available2015-05-19T10:50:50Z
dc.date.issued2013
dc.identifier.citationBezuidenhout, J.J. et al. 2013. Application of evolutionary algorithms to develop a rule set for assessing the rehabilitation status of asbestos mines in South Africa. Environmental earth sciences. 70:3267-3275. [http://link.springer.com/journal/12665]en_US
dc.identifier.issn1866-6280
dc.identifier.issn1866-6299 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/13835
dc.description.abstractAsbestos mining has left a legacy of pollution in former mining areas that continues to negatively affect both the environment and local communities. In 2007, the Rehabilitation Prioritisation Index was developed as a scientific tool to indicate the preferred sequence for mine site rehabilitation and served as a departure point for the present investigation in which a database for the rehabilitation success of asbestos sites was developed. Broad-based quantitative and qualitative data, typically used for monitoring rehabilitation success, including amongst others, soil cover depth, physical and chemical soil properties, microbial activity, vegetation properties and small mammal abundance were analysed using multivariate statistics, specifically a redundancy analysis. The most representative model was subsequently selected for the classification of the rehabilitated sites. The multivariate analysis revealed those factors typically associated with rehabilitation success or failure, as well as essentials to be addressed. The feasibility of development of a rule set for rehabilitated site classification was firstly investigated using neural networks which also assisted in the selection of significant parameters. Results from the neural network approach were then used to guide parameter selection for the evolutionary algorithm software. The coordinate scores for the first two axes of the redundancy analysis served as targets for the evolutionary algorithms. Overall, a targeting match of 71 % for the first axis coordinates and 38 % for the second axis coordinates were obtained. Contributing parameters for the rule set included: Cl, K, pH, percentage organic carbon, Zn, NH4 and SO4 content of the sites.en_US
dc.description.urihttp://link.springer.com/journal/12665
dc.language.isoenen_US
dc.publisherSpringer Linken_US
dc.subjectAsbestos rehabilitationen_US
dc.subjectevolutionary algorithmsen_US
dc.subjectneural network approachen_US
dc.titleApplication of evolutionary algorithms to develop a rule set for assessing the rehabilitation status of asbestos mines in South Africaen_US
dc.typeArticleen_US
dc.contributor.researchID10926542 - Bezuidenhout, Johannes Jacobus
dc.contributor.researchID12775983 - Liebenberg-Weyers, Danica
dc.contributor.researchID11935952 - Claassens, Sarina


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