A Neural Network Based Approach to Elephant Migration Prediction in a South African Game Reserve
Department
Electrical and Computer Engineering
Major
Computer Engineering
Research Advisor
Venayagamoorthy, Ganesh K.
Advisor's Department
Electrical and Computer Engineering
Funding Source
UMSAEP: Computational Intelligence Techniques Applied to Modeling Herbivore Plant Interactions in African Savannahs. National Science Foundation CAREER grant ECS #0348221.
Abstract
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 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 migration prediction.
Biography
Parviz is a senior undergraduate student attending the University of Missouri-Rolla, majoring in computer engineering. After taking a computational intelligence course, he became interested in applications of neural networks. He is currently a research assistant for the Real-Time Power and Intelligent Systems lab.
Research Category
Engineering
Presentation Type
Oral Presentation
Document Type
Presentation
Award
Engineering oral presentation, First place
Presentation Date
12 Apr 2006, 10:30 am
A Neural Network Based Approach to Elephant Migration Prediction in a South African Game Reserve
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 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 migration prediction.
