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 a recurrent neural network (RNN) to continuously 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 RNN-PSO for elephant migration prediction.
P. Palangpour et al., "Recurrent Neural Network Based Predictions of Elephant Migration in a South African Game Reserve," Proceedings of the IEEE International Joint Conference on Neural Network, 2006, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/IJCNN.2006.246953
IEEE International Joint Conference on Neural Network, 2006
Electrical and Computer Engineering
Article - Conference proceedings
© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2006