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LONG Wen-hua, CHEN Hong-han, DUAN Qing-mei, LI Zhi, PAN Hong-jie, LIU Rong-yi. APPLICATION OF ARTIFICIAL NEURAL NETWORK IN THE RESTORATION OF TRITIUM CONCENTRATION IN PRECIPITATION[J]. Geology and Resources, 2008, 17(3): 208-212. DOI: 10.13686/j.cnki.dzyzy.2008.03.016
Citation: LONG Wen-hua, CHEN Hong-han, DUAN Qing-mei, LI Zhi, PAN Hong-jie, LIU Rong-yi. APPLICATION OF ARTIFICIAL NEURAL NETWORK IN THE RESTORATION OF TRITIUM CONCENTRATION IN PRECIPITATION[J]. Geology and Resources, 2008, 17(3): 208-212. DOI: 10.13686/j.cnki.dzyzy.2008.03.016
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APPLICATION OF ARTIFICIAL NEURAL NETWORK IN THE RESTORATION OF TRITIUM CONCENTRATION IN PRECIPITATION

  • 1. China University of Geosciences, Beijing 100083, China;

  • 2. Inner Mongolia Geological Survey, Hohhot 010020, China;

  • 3. Inner Mongolia Institute of Land and Resources Exploration and Development, Hohhot 010020, China;

  • 4. Inner Mongolia Institute of Geo-environment Monitoring, Hohhot 010020, China

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  • Received Date: January 08, 2008
  • Revised Date: May 02, 2008
  • Available Online: December 29, 2022
  • Published Date: September 29, 2008
    Graphical Abstract
    • Abstract
  • The artificial neural networks are able to distinguish the complex nonlinear relations between the input/output data. Based on such characteristics, this article selects IAEA/WMO observation data of tritium concentration in atmospheric precipitation from 70 gauging stations in Northern Hemisphere (latitude 22-74°) to establish the restoration model for the annual average concentration of tritium in atmospheric precipitation. With comparison, it is concluded that, the tritium concentration restored by the artificial neural networks can objectively reflect its true value, which provides a new thought for the datum-free areas to restore the tritium concentration in atmospheric precipitation from 1953.
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