Neural Network Using to Analyze the Results of Environmental Monitoring of Water

被引:2
|
作者
Usachev, V. A. [1 ]
Voronova, L., I [1 ]
Voronov, V., I [1 ]
Zharov, I. A. [1 ]
Strelnikov, V. G. [1 ]
机构
[1] Moscow Tech Univ Commun & Informat, Moscow, Russia
来源
2019 SYSTEMS OF SIGNALS GENERATING AND PROCESSING IN THE FIELD OF ON BOARD COMMUNICATIONS | 2019年
关键词
big data; neural network; machine learning; water quality monitoring; sensors;
D O I
10.1109/sosg.2019.8706733
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Biotrickling filtration of n-butanol vapors: process monitoring using electronic nose and artificial neural network
    Bartosz Szulczyński
    Piotr Rybarczyk
    Milena Gospodarek
    Jacek Gębicki
    Monatshefte für Chemie - Chemical Monthly, 2019, 150 : 1667 - 1673
  • [42] DETECTION AND MONITORING OF BEACH LITTER USING UAV IMAGE AND DEEP NEURAL NETWORK
    Bak, S. H.
    Hwang, D. H.
    Kim, H. M.
    Yoon, H. J.
    ISPRS ICWG III/IVA GI4DM 2019 - GEOINFORMATION FOR DISASTER MANAGEMENT, 2019, 42-3 (W8): : 55 - 58
  • [43] Joint monitoring of the mean and variance of a process by using an artificial neural network approach
    Cheng, CS
    Chen, SJ
    INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE, 2003, 10 (01): : 62 - 72
  • [44] Integrity monitoring method for dry storage casks using artificial neural network
    Kim, Hyong Chol
    Han, Sam Hee
    Lee, Young Jin
    NUCLEAR ENGINEERING AND DESIGN, 2020, 366
  • [45] In-situ monitoring of plasma equipment using spectrophotometric colorimetry and neural network
    Kim, Byungwhan
    Kwon, Minji
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2011, 107 (01) : 90 - 97
  • [46] Power quality monitoring system using wavelet-based neural network
    Kim, H
    Lee, J
    Choi, J
    Lee, S
    Kim, J
    2004 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY - POWERCON, VOLS 1 AND 2, 2004, : 453 - 458
  • [47] The Monitoring of Factory Electrical System in Collapse Using Neural Network Prediction Method
    Boonseng, Chongrag
    Nilnimitr, Nannam
    Kularbphettong, Kunyanuth
    2020 8TH INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS (CMD 2020), 2020, : 270 - 273
  • [48] Sea state estimation using monitoring data by convolutional neural network (CNN)
    Kawai, Toshiki
    Kawamura, Yasumi
    Okada, Tetsuo
    Mitsuyuki, Taiga
    Chen, Xi
    JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, 2021, 26 (03) : 947 - 962
  • [49] Condition Monitoring of Pharmaceutical Autoclave Germs Removal Using Artificial Neural Network
    Badera, Priya
    Jain, S. K.
    Parakh, Arun
    Sharma, Tarun
    2016 11TH INTERNATIONAL CONFERENCE ON INDUSTRIAL AND INFORMATION SYSTEMS (ICIIS), 2016, : 683 - 687
  • [50] Sea state estimation using monitoring data by convolutional neural network (CNN)
    Toshiki Kawai
    Yasumi Kawamura
    Tetsuo Okada
    Taiga Mitsuyuki
    Xi Chen
    Journal of Marine Science and Technology, 2021, 26 : 947 - 962