Knowledge Discovery using Artificial Neural Networks for a Conservation Biology Domain.
Rohitash Chandra, Christian Walter Peter Omlin · 2007
Abstract- We present an artificial intelligence method for the development of decision support systems for environmental management and demonstrate its strengths using an example from the domain of biodiversity and conservation biology. Renosterveld vegetation is unique to South Africa; it is under threat of extinction as a result of rapidly growing agricultural activities. We use artificial neural networks and decision trees for knowledge discovery on the Renosterveld domain. We train artificial neural networks on a dataset of the existing plant species and show their generalization performance with gradient descent learning. We then extract knowledge from trained neural networks in the form of decision trees and obtain rules which describe the existence of the remaining Renosterveld vegetation. These rules will be used as a contribution for the conservation of Renosterveld. The rules demonstrate a prediction of 78 % that a Renosterveld plant will grow in a particular environment given its environmental conditions. The general paradigm can hence be applied to other plant species for knowledge discovery and the development of decision support systems.