Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/10484
AuthorsBrancato, A.* 
Buscema, P. M.* 
Massini, G.* 
Gresta, S.* 
TitlePattern Recognition for Flank Eruption Forecasting: An Application at Mount Etna Volcano (Sicily, Italy)
Issue DateJul-2016
Series/Report no./6 (2016)
DOI10.4236/ojg.2016.67046
URIhttp://hdl.handle.net/2122/10484
KeywordsMt. Etna Volcano, Flank Eruption Forecasting, Neural Networks, Pattern Recognition, Monitoring Data
Subject Classification04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring 
AbstractA volcano can be defined as a complex system, not least for the hidden clues related to its internal nature. Innovative models grounded in the Artificial Sciences, have been proposed for a novel pattern recognition analysis at Mt. Etna volcano. The reference monitoring dataset dealt with real data of 28 parameters collected between January 2001 and April 2005, during which the volcano underwent the July-August 2001, October 2002-January 2003 and September 2004-April 2005 flank eruptions. There were 301 eruptive days out of an overall number of 1581 investigated days. The analysis involved successive steps. First, the TWIST algorithm was used to select the most predictive attributes associated with the flank eruption target. During his work, the algorithm TWIST selected 11 characteristics of the input vector: among them SO2 and CO2 emissions, and also many other attributes whose linear correlation with the target was very low. A 5 × 2 Cross Valida- tion protocol estimated the sensitivity and specificity of pattern recognition algorithms. Finally, different classification algorithms have been compared to understand if this pattern recognition task may have suitable results and which algorithm performs best. Best results (higher than 97% accuracy) have been obtained after performing advanced Artificial Neural Networks, with a sensi- tivity and specificity estimates over 97% and 98%, respectively. The present analysis highlights that a suitable monitoring dataset inferred hidden information about volcanic phenomena, whose highly non-linear processes are enhanced.
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