Scholars' Mine
Missouri S&T
Research Repository
Curtis Laws Wilson Library
400 W. 14th Street
Rolla, MO 65409-0060
scholarsmine@mst.edu
| Title: | Recurrent neural network based predictions of elephant migration in a South African game reserve | |
| Author (s): | Palangpour, P. Venayagamoorthy, Ganesh K. Duffy, K. | |
| Department/Lab Affiliations: | Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory | |
| Issue Date: | 2006 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Palangpour, P.; Venayagamoorthy, G.K.; Duffy, K. "Recurrent Neural Network Based Predictions of Elephant Migration in a South African Game Reserve" IJCNN '06. International Joint Conference on Neural Networks, 2006. 16-21 July 2006 Pages: 4084- 4088 | |
| 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 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. | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
| Publisher URL: | ||
| Link to this page: | ||
| Full Text: |
|
| title | Recurrent neural network based predictions of elephant migration in a South African game reserve | |
| contributor.author | Palangpour, P. | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| contributor.author | Duffy, K. | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Real-Time Power and Intelligent Systems Laboratory | |
| date.issued | 2006 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Palangpour, P.; Venayagamoorthy, G.K.; Duffy, K. "Recurrent Neural Network Based Predictions of Elephant Migration in a South African Game Reserve" IJCNN '06. International Joint Conference on Neural Networks, 2006. 16-21 July 2006 Pages: 4084- 4088 | |
| identifier.pub.URI | ||
| description.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 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. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:28:22Z | |
| date.available | 2007-04-05T14:28:21Z | |
| identifier.persist.URI | ||
| Full Text |
|