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Proceedings Paper

Automated information-analytical system for thunderstorm monitoring and early warning alarms using modern physical sensors and information technologies with elements of artificial intelligence
Author(s): Anton S. Boldyreff; Dmitry A. Bespalov; Anatoly Kh. Adzhiev
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Paper Abstract

Methods of artificial intelligence are a good solution for weather phenomena forecasting. They allow to process a large amount of diverse data. Recirculation Neural Networks is implemented in the paper for the system of thunderstorm events prediction. Large amounts of experimental data from lightning sensors and electric field mills networks are received and analyzed. The average recognition accuracy of sensor signals is calculated. It is shown that Recirculation Neural Networks is a promising solution in the forecasting of thunderstorms and weather phenomena, characterized by the high efficiency of the recognition elements of the sensor signals, allows to compress images and highlight their characteristic features for subsequent recognition.

Paper Details

Date Published: 16 May 2017
PDF: 7 pages
Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 102180P (16 May 2017); doi: 10.1117/12.2279848
Show Author Affiliations
Anton S. Boldyreff, Southern Federal Univ. (Russian Federation)
Dmitry A. Bespalov, Southern Federal Univ. (Russian Federation)
Anatoly Kh. Adzhiev, High-Mountain Geophysical Institute (Russian Federation)

Published in SPIE Proceedings Vol. 10218:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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