{"title":"木材管胞长径比预测方法的比较","authors":"Guangsheng Chen, Li Ge","doi":"10.1109/DBTA.2010.5658959","DOIUrl":null,"url":null,"abstract":"With the decrease of nature forest in all over the world, plantation and improving wood utilization becomes important. The study on forecast of wood properties can provide a scientific basis for intensive farming of plantation and targeted utilization of wood resource. The purpose of this paper is to establish a forecast model of wood properties and to evaluate wood qualities comprehensively. Tracheid aspect ratio was selected as analytical data. The feasibility of wood properties forecasting model was established by the methods of linear regression, time series, and neural network and its forecasting precision were compared and proof-tested. Results indicated that the forecasting model established by neural network method was the best, with the smallest error and best precision.","PeriodicalId":320509,"journal":{"name":"2010 2nd International Workshop on Database Technology and Applications","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Comparison of Forecasting Methods for Aspect Ratio of Wood Tracheid\",\"authors\":\"Guangsheng Chen, Li Ge\",\"doi\":\"10.1109/DBTA.2010.5658959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the decrease of nature forest in all over the world, plantation and improving wood utilization becomes important. The study on forecast of wood properties can provide a scientific basis for intensive farming of plantation and targeted utilization of wood resource. The purpose of this paper is to establish a forecast model of wood properties and to evaluate wood qualities comprehensively. Tracheid aspect ratio was selected as analytical data. The feasibility of wood properties forecasting model was established by the methods of linear regression, time series, and neural network and its forecasting precision were compared and proof-tested. Results indicated that the forecasting model established by neural network method was the best, with the smallest error and best precision.\",\"PeriodicalId\":320509,\"journal\":{\"name\":\"2010 2nd International Workshop on Database Technology and Applications\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Database Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DBTA.2010.5658959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Workshop on Database Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DBTA.2010.5658959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of Forecasting Methods for Aspect Ratio of Wood Tracheid
With the decrease of nature forest in all over the world, plantation and improving wood utilization becomes important. The study on forecast of wood properties can provide a scientific basis for intensive farming of plantation and targeted utilization of wood resource. The purpose of this paper is to establish a forecast model of wood properties and to evaluate wood qualities comprehensively. Tracheid aspect ratio was selected as analytical data. The feasibility of wood properties forecasting model was established by the methods of linear regression, time series, and neural network and its forecasting precision were compared and proof-tested. Results indicated that the forecasting model established by neural network method was the best, with the smallest error and best precision.