V. Usachev, L. Voronova, V. Voronov, I. Zharov, Vladimir G. Strelnikov
{"title":"Neural Network Using to Analyze the Results of Environmental Monitoring of Water","authors":"V. Usachev, L. Voronova, V. Voronov, I. Zharov, Vladimir G. Strelnikov","doi":"10.1109/SOSG.2019.8706733","DOIUrl":null,"url":null,"abstract":"The acuteness of the environmental tracking problem is constantly growing. Currently, environmental issues are analyzed using big data. Many open data sources (Kaggle, Open Data Portal of the Russian Federation, etc.) contain a variety of environmental information. Based on the data and using the tools for analyzing big data and machine learning, a system has been developed that simulates the state of water quality in the Moscow waters. On the basis of the indicators obtained, the neural network was trained, which classifies the state of the reservoir into good and deviant.","PeriodicalId":418978,"journal":{"name":"2019 Systems of Signals Generating and Processing in the Field of on Board Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Systems of Signals Generating and Processing in the Field of on Board Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOSG.2019.8706733","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
The acuteness of the environmental tracking problem is constantly growing. Currently, environmental issues are analyzed using big data. Many open data sources (Kaggle, Open Data Portal of the Russian Federation, etc.) contain a variety of environmental information. Based on the data and using the tools for analyzing big data and machine learning, a system has been developed that simulates the state of water quality in the Moscow waters. On the basis of the indicators obtained, the neural network was trained, which classifies the state of the reservoir into good and deviant.
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