K-CM application for supervised pattern recognition at Mt. Etna: an innovative tool to forecast flank eruptive activity

We investigated the relationship between the temporal monitoring series routinely recorded at Mt. Etna and the flank eruptions
that occurred between January 2001 and April 2005 by the K-contractive map (K-CM) method pattern classifier with supervised
learning. The reference dataset includes 28 variables and 1580 records collected over 52 months for a total of 301 eruptive days.
A two-step analysis was performed. In the first step analysis, we used the 28 parameters of each day to recognize anomalies
heralding a flank eruption. K-CM estimated a sensitivity higher than 95% and a specificity close to 100%. In the second step
analysis, we considered each record comprising the 28 variables for 6 days as an input (for a total of 180 inputs) and the outcomes
of the seventh day as an output to predict eruption or rest. In this case, K-CM showed sensitivity and specificity close to 98%and
100%, respectively. Results highlight the reliability of the K-CM method to build up a prediction algorithm able to alert the
volcano experts a day before the occurrence of a potential flank eruption. The robustness of the two analyses was investigated by
the behavior of the receiver operating characteristic curve. The relative area under the curve showed values close to 1, thus
providing a valid measure of the performance of the classifier. Finally, a complete overview of the performance levels of the
method used was explored analyzing the retrieved Molchan error diagram, in both cases, trajectories very close to the theoretical
minimum.