Tellez Ricardo Delgado1; Wang Shaohua2; Zhong Ershun2; Cai Wenwen3; Long Liang2
Source Publicationjournalofresourcesandecology
AbstractThis paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms (CLG–LUSA). It uses Kohonen's Self Organized Maps (SOM) and Linear Vector Quantization (LVQ) among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision's attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a municipality of Cuba. CLG–LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making.
Document Type期刊论文
3.SuperMap Software Co. Ltd.
Recommended Citation
GB/T 7714
Tellez Ricardo Delgado,Wang Shaohua,Zhong Ershun,et al. competitivelearningapproachtogisbasedlandusesuitabilityanalysis[J]. journalofresourcesandecology,2016,7(6):430.
APA Tellez Ricardo Delgado,Wang Shaohua,Zhong Ershun,Cai Wenwen,&Long Liang.(2016).competitivelearningapproachtogisbasedlandusesuitabilityanalysis.journalofresourcesandecology,7(6),430.
MLA Tellez Ricardo Delgado,et al."competitivelearningapproachtogisbasedlandusesuitabilityanalysis".journalofresourcesandecology 7.6(2016):430.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tellez Ricardo Delgado]'s Articles
[Wang Shaohua]'s Articles
[Zhong Ershun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tellez Ricardo Delgado]'s Articles
[Wang Shaohua]'s Articles
[Zhong Ershun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tellez Ricardo Delgado]'s Articles
[Wang Shaohua]'s Articles
[Zhong Ershun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.