{"title":"基于太阳x射线耀斑数据的神经网络预测noaa尺度太阳质子事件","authors":"E. Jeong, Jin-Yi Lee, Y. Moon, Jong-Yeop Park","doi":"10.5303/JKAS.2014.47.6.209","DOIUrl":null,"url":null,"abstract":"In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.","PeriodicalId":49994,"journal":{"name":"Journal of the Korean Astronomical Society","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2014-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"FORECAST OF SOLAR PROTON EVENTS WITH NOAA SCALES BASED ON SOLAR X-RAY FLARE DATA USING NEURAL NETWORK\",\"authors\":\"E. Jeong, Jin-Yi Lee, Y. Moon, Jong-Yeop Park\",\"doi\":\"10.5303/JKAS.2014.47.6.209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.\",\"PeriodicalId\":49994,\"journal\":{\"name\":\"Journal of the Korean Astronomical Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2014-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Korean Astronomical Society\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.5303/JKAS.2014.47.6.209\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Astronomical Society","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.5303/JKAS.2014.47.6.209","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
FORECAST OF SOLAR PROTON EVENTS WITH NOAA SCALES BASED ON SOLAR X-RAY FLARE DATA USING NEURAL NETWORK
In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.
期刊介绍:
JKAS is an international scientific journal publishing papers in all fields of astronomy and astrophysics. All manuscripts are subject to the scrutiny of referees. Manuscripts submitted to JKAS must comply with the ethics policy of JKAS. Six regular issues are published each year on February 28, April 30, June 30, August 31, October 31, and December 31. One year''s issues compose one volume.