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

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 norma...

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Bibliographic Details
Published in:International journal of system assurance engineering and management 2020-05, Vol.11 (Suppl 1), p.1-9
Main Authors: Grebnev, Yaroslav, Moskalev, Alexander, Vershkov, Anatoliy, Gazizulina, Albina
Format: Article
Language:English
Subjects:
Algorithms
Engineering
Engineering Economics
Forecasting
Forest & brush fires
Forest fires
Logistics
Marketing
Mathematical models
Organization
Original Article
Quality Control
Reliability
Safety and Risk
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Summary: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.
ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-019-00786-w