TITLE:
Pattern Recognition for Flank Eruption Forecasting: An Application at Mount Etna Volcano (Sicily, Italy)
AUTHORS:
A. Brancato, P. M. Buscema, G. Massini, S. Gresta
KEYWORDS:
Mt. Etna Volcano, Flank Eruption Forecasting, Neural Networks, Pattern Recognition, Monitoring Data
JOURNAL NAME:
Open Journal of Geology,
Vol.6 No.7,
July
26,
2016
ABSTRACT: A
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 Validation 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 sensitivity 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.