Strategies for the optimal classification of volcanic ash granulometry

The ash fall-out following explosion activity of volcanoes like the Mount Etna, represents a serious hazard for the safety of air traffic, causing, in many cases, flight cancellations or temporary closures of the airport with consequently inconvenience for passengers and loss of profit for airlines. Researchers at DIEEI of the University of Catania, in the framework of the SECESTA project have developed a low-cost smart multisensor system for the monitoring of ash fall-out phenomenon by measuring ash presence, average granulometry and ash flow-rate. The node, is intended to be integrated into a sensor network which will provide a distributed information useful to predict the time-space evolution and oriented to the implementation of an early warning approach for the monitoring of the phenomenon. This paper is particularly focused on the methodologies to be adopted for the choice of the optimal granulometry classification thresholds by using the ROC curves theory Experimental investigations have been performed using ash erupted by Etna volcano.