{"title":"使用模糊集建模的南摩拉维亚地区自行车道难度等级","authors":"Pavel Kolisko","doi":"10.14311/GI.11.1","DOIUrl":null,"url":null,"abstract":"The fuzzy sets are more suitable for modelling of the vagueness than the classical crisp sets. They present vague phenomenon and relations which are not exactly bounded but they are associated with their verbal expression. Inaccuracies of characteristics of the bike trail difficulty are connected to the area changes and it is necessary to evaluate and update them regularly. The analysis is solved by the compositional rule of inference methods especially by Mamdani’s and Larsen’s method. The difficulty is the result of rules processing with verbal variables for the type of road and slope. The suitability of methods is tested by certified and categorized parts of the bike trails. The modelling has been performed by rasters using software ArcGIS 10.1 and its geoprocessing tools.","PeriodicalId":436054,"journal":{"name":"Geoinformatics FCE CTU","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Bike Trail Difficulty Rating in the South Moravian Region Modelled Using Fuzzy Sets\",\"authors\":\"Pavel Kolisko\",\"doi\":\"10.14311/GI.11.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The fuzzy sets are more suitable for modelling of the vagueness than the classical crisp sets. They present vague phenomenon and relations which are not exactly bounded but they are associated with their verbal expression. Inaccuracies of characteristics of the bike trail difficulty are connected to the area changes and it is necessary to evaluate and update them regularly. The analysis is solved by the compositional rule of inference methods especially by Mamdani’s and Larsen’s method. The difficulty is the result of rules processing with verbal variables for the type of road and slope. The suitability of methods is tested by certified and categorized parts of the bike trails. The modelling has been performed by rasters using software ArcGIS 10.1 and its geoprocessing tools.\",\"PeriodicalId\":436054,\"journal\":{\"name\":\"Geoinformatics FCE CTU\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoinformatics FCE CTU\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14311/GI.11.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoinformatics FCE CTU","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14311/GI.11.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bike Trail Difficulty Rating in the South Moravian Region Modelled Using Fuzzy Sets
The fuzzy sets are more suitable for modelling of the vagueness than the classical crisp sets. They present vague phenomenon and relations which are not exactly bounded but they are associated with their verbal expression. Inaccuracies of characteristics of the bike trail difficulty are connected to the area changes and it is necessary to evaluate and update them regularly. The analysis is solved by the compositional rule of inference methods especially by Mamdani’s and Larsen’s method. The difficulty is the result of rules processing with verbal variables for the type of road and slope. The suitability of methods is tested by certified and categorized parts of the bike trails. The modelling has been performed by rasters using software ArcGIS 10.1 and its geoprocessing tools.