Time-Delay Neural Network Based Predictions of Elephant Distribution in a South African Game Reserve

Parviz Palangpour
Ganesh K. Venayagamoorthy, Missouri University of Science and Technology
Kevin Duffy

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1921

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A large portion of South Africa's elephant population can be found on small wildlife reserves. When confined to enclosed reserves the elephant densities are much higher than observed in the wild. The large nutritional demands and destructive foraging behavior of elephants threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of the reserve as well as which area they will move to next is essential. The goal of this study is to train an artificial neural network to predict an elephant herd's next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing future impact of elephant populations on different areas of the reserve. The particle swarm optimization (PSO) algorithm is used to adapt the weights of the neural network. Results are presented to show the effectiveness of TDNN-PSO for elephant distribution prediction.