Application of artificical neural network in complex systems of regional sustainable development
Shi, C, Guo, ZY and James, P 2004, 'Application of artificical neural network in complex systems of regional sustainable development' , Chinese Geographical Science, 14 (1) , pp. 1-8.
| PDF - Published Version Restricted to Repository staff only Download (762kB) | Request a copy |
Abstract
Meeting the challenge of sustainable development requires substantial advances in understanding the interaction of natural and human systems. The dynamics of regional sustainable development could be addressed in the context of complex system thinking. Three features of complex systems are that they are uncertain, non-linear and self-organizing. Modeling regional development requires a consideration of these features. This paper discusses the feasibility of using the artificial neural networt(ANN) to establish an adjustment prediction model for the complex systems of sustainable development (CSSD). Shanghai Municipality was selected as the research area to set up the model, from which reliable prediction data were produced in order to help regional development planning. A new approach, which could help to manage regional sustainable development, is then explored.
| Item Type: | Article |
|---|---|
| Themes: | Subjects / Themes > Q Science > QH Natural history Subjects / Themes > Q Science > QH Natural history > QH001 General, inc. conservation, geographical distribution Subjects outside of the University Themes |
| Schools: | Colleges and Schools > College of Science & Technology Colleges and Schools > College of Science & Technology > School of Environment and Life Sciences Colleges and Schools > College of Science & Technology > School of Environment and Life Sciences > Ecosystems and Environment Research Centre |
| Journal or Publication Title: | Chinese Geographical Science |
| Publisher: | Science Press |
| Refereed: | Yes |
| ISSN: | 1002-0063 |
| Depositing User: | Users 29196 not found. |
| Date Deposited: | 27 Oct 2010 15:28 |
| Last Modified: | 08 Mar 2012 09:52 |
| URI: | http://usir.salford.ac.uk/id/eprint/11376 |
Document Downloads
More statistics for this item...Actions (login required)
| Edit record (repository staff only) |

Tools
Tools