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The practice of connectionist model for predicting forest fires in the Arctic zones of the Krasnoyarsk Territory

Author

Listed:
  • Yaroslav Grebnev

    (Siberian Federal University
    Center for Crisis Management of the Head Office of the Emercom of Russia in the Krasnoyarsk Territory)

  • Alexander Moskalev

    (Siberian Federal University)

  • Anatoliy Vershkov

    (Siberian Federal University)

  • Albina Gazizulina

    (Peter the Great St. Petersburg Polytechnic University)

Abstract

This paper presents data on the localization and number of forest fires that occur in the Arctic zones of the Krasnoyarsk Territory, as well as the possible causes of their occurrence. It is established that the main cause of forest and landscape fires are natural phenomena. A database of data normalized in a certain way about the factors shaping the occurrence of natural forest and landscape fires has been formed. The practice of connectionist algorithms, a forecasting model has been developed, and, based on data on forest and landscape fires in the Krasnoyarsk Territory; a model has been evaluated for using the model to predict fires in 2018. In order to compile a forecast of forest and landscape fires for 2019 in the Arctic zones of the Krasnoyarsk Territory using the connectionist algorithms, an optimal neuroarchitecture was chosen, which allows long-term time series forecasting.

Suggested Citation

  • Yaroslav Grebnev & Alexander Moskalev & Anatoliy Vershkov & Albina Gazizulina, 0. "The practice of connectionist model for predicting forest fires in the Arctic zones of the Krasnoyarsk Territory," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-9.
    • Handle: RePEc:spr:ijsaem:v::y::i::d:10.1007_s13198-019-00786-w
    DOI: 10.1007/s13198-019-00786-w
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