{"title":"影响近地大气细颗粒物密度的气候数据因子模拟","authors":"A. Ghobakhlou, S. Zandi, P. Sallis","doi":"10.1109/AMS.2017.15","DOIUrl":null,"url":null,"abstract":"this paper describes the relationship of climate toatmospheric particulate matter. The climate factors ofprecipitation, humidity, temperature and wind speed aremapped to the fine-particulate substances measured as being 2.5micrometers in diameter (PM2.5). Using the climate variablesas indicators, the paper illustrates a method for estimating theconcentration potential for PM2.5 in the near-groundatmosphere. The preferred method described is selected fromthree analytical approaches compared using a common data set.The three methods used are Multiple Linear Regression (MLR),Multilayer Perceptron (MLP) and Fuzzy Neural Networksmetho","PeriodicalId":219494,"journal":{"name":"2017 Asia Modelling Symposium (AMS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modelling Climate Data Factors Influencing Fine-Particulate Matter Density in the Near-Ground Atmosphere\",\"authors\":\"A. Ghobakhlou, S. Zandi, P. Sallis\",\"doi\":\"10.1109/AMS.2017.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"this paper describes the relationship of climate toatmospheric particulate matter. The climate factors ofprecipitation, humidity, temperature and wind speed aremapped to the fine-particulate substances measured as being 2.5micrometers in diameter (PM2.5). Using the climate variablesas indicators, the paper illustrates a method for estimating theconcentration potential for PM2.5 in the near-groundatmosphere. The preferred method described is selected fromthree analytical approaches compared using a common data set.The three methods used are Multiple Linear Regression (MLR),Multilayer Perceptron (MLP) and Fuzzy Neural Networksmetho\",\"PeriodicalId\":219494,\"journal\":{\"name\":\"2017 Asia Modelling Symposium (AMS)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Asia Modelling Symposium (AMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AMS.2017.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Asia Modelling Symposium (AMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AMS.2017.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modelling Climate Data Factors Influencing Fine-Particulate Matter Density in the Near-Ground Atmosphere
this paper describes the relationship of climate toatmospheric particulate matter. The climate factors ofprecipitation, humidity, temperature and wind speed aremapped to the fine-particulate substances measured as being 2.5micrometers in diameter (PM2.5). Using the climate variablesas indicators, the paper illustrates a method for estimating theconcentration potential for PM2.5 in the near-groundatmosphere. The preferred method described is selected fromthree analytical approaches compared using a common data set.The three methods used are Multiple Linear Regression (MLR),Multilayer Perceptron (MLP) and Fuzzy Neural Networksmetho
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