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

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.

Keywords

• neural networks
• volcanology
• fuzzy logic
• LSTM