{"title":"基于多元线性模型的水物理数据时空分析","authors":"V. Rukšėnienė, K. Dučinskas, I. Dailidienė","doi":"10.1109/BALTIC.2014.6887887","DOIUrl":null,"url":null,"abstract":"The subjects of this research are the surface layer hydrophysical parameters and their spatial-temporal statistical models in the south-eastern Baltic Sea. Here we analyze sea surface water temperature (SST), water salinity and ice phenomena data collected in the period 2009-2012. The Center of Marine Research in Klaipėda (Lithuania) provides us with the data. The purpose of this research is to construct optimal parametric spatial trend and spatial variation (semivariogram) models at different time layers. To use constructed models for ice formation statistical dependence on water salinity and temperature research, also to interpolate and to predict using different linear prediction models (kriging).","PeriodicalId":435850,"journal":{"name":"2014 IEEE/OES Baltic International Symposium (BALTIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial-temporal analysis of hydrophysical data by using multiple linear models\",\"authors\":\"V. Rukšėnienė, K. Dučinskas, I. Dailidienė\",\"doi\":\"10.1109/BALTIC.2014.6887887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The subjects of this research are the surface layer hydrophysical parameters and their spatial-temporal statistical models in the south-eastern Baltic Sea. Here we analyze sea surface water temperature (SST), water salinity and ice phenomena data collected in the period 2009-2012. The Center of Marine Research in Klaipėda (Lithuania) provides us with the data. The purpose of this research is to construct optimal parametric spatial trend and spatial variation (semivariogram) models at different time layers. To use constructed models for ice formation statistical dependence on water salinity and temperature research, also to interpolate and to predict using different linear prediction models (kriging).\",\"PeriodicalId\":435850,\"journal\":{\"name\":\"2014 IEEE/OES Baltic International Symposium (BALTIC)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE/OES Baltic International Symposium (BALTIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BALTIC.2014.6887887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE/OES Baltic International Symposium (BALTIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BALTIC.2014.6887887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spatial-temporal analysis of hydrophysical data by using multiple linear models
The subjects of this research are the surface layer hydrophysical parameters and their spatial-temporal statistical models in the south-eastern Baltic Sea. Here we analyze sea surface water temperature (SST), water salinity and ice phenomena data collected in the period 2009-2012. The Center of Marine Research in Klaipėda (Lithuania) provides us with the data. The purpose of this research is to construct optimal parametric spatial trend and spatial variation (semivariogram) models at different time layers. To use constructed models for ice formation statistical dependence on water salinity and temperature research, also to interpolate and to predict using different linear prediction models (kriging).