Forecasting SO2 emission of Kilauea volcano using intelligent method of data analysis

Authors

  • Stanislav Zabielin Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0000-0003-2178-7415

DOI:

https://doi.org/10.20535/SRIT.2308-8893.2019.4.03

Keywords:

neural networks, volcanology, fuzzy logic, LSTM

Abstract

Kilauea is one of the most active and well-known volcanoes in the world and most of our knowledge of volcanism originates from its research. During a long study of volcanoes, many different methods of forecasting their activity were proposed, from the seismological analysis to the statistical analysis of their emissions. However, a comprehensive analysis of data arrays with the help of intelligent methods of data analysis has not been carried out before. Using fuzzy data processing methods, a neural network, volcanic and atmospheric indicators, we forecast emissions SO2 for a period of one to three months.

Author Biography

Stanislav Zabielin, Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Stanislav Zabielin,

a Ph.D. student at the Department of Mathematical Methods of System Analysis of Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine.

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Published

2019-12-23

Issue

Section

Progressive information technologies, high-efficiency computer systems