Muhammad Irfan , Aleksandra Koj , Majid Sedighi , Hywel Thomas
{"title":"基于人工智能和多准则决策分析的通用空间决策支持系统的设计与开发","authors":"Muhammad Irfan , Aleksandra Koj , Majid Sedighi , Hywel Thomas","doi":"10.1016/j.grj.2017.08.003","DOIUrl":null,"url":null,"abstract":"<div><p><span>A new integrated and generic Spatial Decision Support System (SDSS) is presented based on a combination of Artificial Intelligence and Multicriteria Decision Analysis techniques. The approach proposed is developed to address commonly faced spatial decision problems of site selection, site ranking, impact assessment and spatial knowledge discovery under one system. The site selection module utilises a theme-based </span>Analytical Hierarchy Process<span>. Two novel site ranking techniques are introduced. The first is based on a systematic neighbourhood comparison of sites with respect to key datasets (criterions). The second utilises multivariate ordering capability of one-dimensional Self-Organizing Maps. The site impact assessment module utilises a new spatially enabled Rapid Impact Assessment Matrix. A spatial variant of General Regression Neural Networks is developed for Geographically Weighted Regression (GWR) and prediction analysis. The developed system is proposed as a useful modern tool that facilitates quantitative and evidence based decision making in multicriteria decision environment. The intended users of the system are decision makers in government organisations, in particular those involved in planning and development when taking into account socio-economic, environmental and public health related issues.</span></p></div>","PeriodicalId":93099,"journal":{"name":"GeoResJ","volume":"14 ","pages":"Pages 47-58"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.grj.2017.08.003","citationCount":"11","resultStr":"{\"title\":\"Design and development of a generic spatial decision support system, based on artificial intelligence and multicriteria decision analysis\",\"authors\":\"Muhammad Irfan , Aleksandra Koj , Majid Sedighi , Hywel Thomas\",\"doi\":\"10.1016/j.grj.2017.08.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>A new integrated and generic Spatial Decision Support System (SDSS) is presented based on a combination of Artificial Intelligence and Multicriteria Decision Analysis techniques. The approach proposed is developed to address commonly faced spatial decision problems of site selection, site ranking, impact assessment and spatial knowledge discovery under one system. The site selection module utilises a theme-based </span>Analytical Hierarchy Process<span>. Two novel site ranking techniques are introduced. The first is based on a systematic neighbourhood comparison of sites with respect to key datasets (criterions). The second utilises multivariate ordering capability of one-dimensional Self-Organizing Maps. The site impact assessment module utilises a new spatially enabled Rapid Impact Assessment Matrix. A spatial variant of General Regression Neural Networks is developed for Geographically Weighted Regression (GWR) and prediction analysis. The developed system is proposed as a useful modern tool that facilitates quantitative and evidence based decision making in multicriteria decision environment. The intended users of the system are decision makers in government organisations, in particular those involved in planning and development when taking into account socio-economic, environmental and public health related issues.</span></p></div>\",\"PeriodicalId\":93099,\"journal\":{\"name\":\"GeoResJ\",\"volume\":\"14 \",\"pages\":\"Pages 47-58\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.grj.2017.08.003\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GeoResJ\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214242817300384\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GeoResJ","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214242817300384","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design and development of a generic spatial decision support system, based on artificial intelligence and multicriteria decision analysis
A new integrated and generic Spatial Decision Support System (SDSS) is presented based on a combination of Artificial Intelligence and Multicriteria Decision Analysis techniques. The approach proposed is developed to address commonly faced spatial decision problems of site selection, site ranking, impact assessment and spatial knowledge discovery under one system. The site selection module utilises a theme-based Analytical Hierarchy Process. Two novel site ranking techniques are introduced. The first is based on a systematic neighbourhood comparison of sites with respect to key datasets (criterions). The second utilises multivariate ordering capability of one-dimensional Self-Organizing Maps. The site impact assessment module utilises a new spatially enabled Rapid Impact Assessment Matrix. A spatial variant of General Regression Neural Networks is developed for Geographically Weighted Regression (GWR) and prediction analysis. The developed system is proposed as a useful modern tool that facilitates quantitative and evidence based decision making in multicriteria decision environment. The intended users of the system are decision makers in government organisations, in particular those involved in planning and development when taking into account socio-economic, environmental and public health related issues.