|NUKLEONIKA 2010, 55(4):501-505
IDENTIFICATION OF RADON ANOMALIES IN SOIL GAS USING DECISION TREES AND NEURAL NETWORKS
Boris Zmazek, Sašo Džeroski, Drago Torkar, Janja Vaupotič, Ivan Kobal
Jožef Stefan Institute, 39 Jamova Str., 1000 Ljubljana, Slovenia
The time series of radon (222Rn) concentration in soil gas at a fault, together with the environmental parameters, have been analysed applying two machine learning techniques: (i) decision trees and (ii) neural networks, with the aim at identifying radon anomalies caused by seismic events and not simply ascribed to the effect of the environmental parameters. By applying neural networks, 10 radon anomalies were observed for 12 earthquakes, while with decision trees, the anomaly was found for every earthquake, but, undesirably, some anomalies appeared also during periods without earthquakes.