IGSNRR OpenIR
competitivelearningapproachtogisbasedlandusesuitabilityanalysis
Tellez Ricardo Delgado1; Wang Shaohua2; Zhong Ershun2; Cai Wenwen3; Long Liang2
2016
Source Publicationjournalofresourcesandecology
ISSN1674-764X
Volume7Issue:6Pages:430
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.
Language英语
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/121784
Collection中国科学院地理科学与资源研究所
Affiliation1.中国科学院大学
2.中国科学院地理科学与资源研究所
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.
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