This 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 Organ- ...This 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 Organ- ized 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 mu- nicipality 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.展开更多
基金partially supported by project 2009DFA13000 funded by the Ministry of Science and Technology of the People’s Republic of ChinaBeijing science and technology projects(Z151100003615012,Z151100003115007)+4 种基金Independent research project of State Key Laboratory of Resources and Environmental Information System(088RAC00YA)Surveying and mapping project of public welfare(201512015)Project of Beijing Excellent Talents(201500002685XG242)National Postdoctoral International Exchange Program(Grant No.20150081)National Natural Science Foundation of China(Grant No.41101116,41271546)
文摘This 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 Organ- ized 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 mu- nicipality 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.
