Renosterveld conservation in South Africa: A case study for handling uncertainty in knowledge-based neural networks for environmental management

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dc.contributor.authorChandra, R.
dc.contributor.authorKnight, Richard
dc.contributor.authorOmlin, C. W.
dc.date.accessioned2023-06-06T08:00:31Z
dc.date.available2023-06-06T08:00:31Z
dc.date.issued2009
dc.description.abstractThis work presents an artificial intelligence method for the development of decision support systems for environmental management and demonstrates its strengths using an example from the domain of biodiversity and conservation biology. The approach takes into account local expert knowledge together with collected field data about plant habitats in order to identify areas which show potential for conserving thriving areas of Renosterveld vegetation and areas that are best suited for agriculture. The available data is limited and cannot be adequately explained by expert knowledge alone. The paradigm combines expert knowledge about the local conditions with the collected ground truth in a knowledge-based neural network. The integration of symbolic knowledge with artificial neural networks is becoming an increasingly popular paradigm for solving real-world applications. The paradigm provides means for using prior knowledge to determine the network architecture, to program a subset of weights to induce a learning bias which guides network training, and to extract knowledge from trained networks; it thus provides a methodology for dealing with uncertainty in the prior knowledge. The role of neural networks then becomes that of knowledge refinement. The open question on how to determine the strength of the inductive bias of programmed weights is addressed by presenting a heuristic which takes the network architecture and training algorithm, the prior knowledge, and the training data into consideration.en_US
dc.identifier.citationChandra, R. et al. (2009). Renosterveld conservation in South Africa: A case study for handling uncertainty in knowledge-based neural networks for environmental management. Journal of Environmental Informatics, 13 (1) ,56-65. 10.3808/jei.200900140en_US
dc.identifier.issn1684-8799
dc.identifier.uri10.3808/jei.200900140
dc.identifier.urihttp://hdl.handle.net/10566/9027
dc.language.isoenen_US
dc.publisherInternational Society for Environmental Information Sciencesen_US
dc.subjectBiodiversityen_US
dc.subjectConservationen_US
dc.subjectSouth Africaen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectBiologyen_US
dc.titleRenosterveld conservation in South Africa: A case study for handling uncertainty in knowledge-based neural networks for environmental managementen_US
dc.typeArticleen_US

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