Identification of radon anomalies in soil gas using decision trees and neural networks

• Autorzy: Zmazek B. , Džeroski S. , Torkar D. , Vaupotič J. , Kobal I.
• Konferencja: Proceedings of the International Conference "Radon in Environment" 10-14 May 2009, Zakopane Poland
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

radon; soil gas; anomalies; decision tree; artificial neural network; earthquakes

• Wydawca: Institute of Nuclear Chemistry and Technology
• Czasopismo: Nukleonika
• Rocznik: 2010
• Tom: Vol. 55, No. 4
• Strony: 501--505
• Opis fizyczny: BIbliogr. 12 poz., rys.

Twórcy

autor

Zmazek B.

autor

Džeroski S.

autor

Torkar D.

autor

Vaupotič J.

autor

Kobal I.

• Jožef Stefan Institute, 39 Jamova Str., 1000 Ljubljana, Slovenia, Tel.: [phone], Fax: [phone],

Bibliografia

• 1. Belyaev AA (2001) Specific features of radon earthquake precursors. Geochem Int 12:1245–1250
• 2. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont
• 3. Di Bello G, Ragosta M, Heinicke J et al. (1998) Time dynamics of background noise in geoelectrical and geochemical signals: an application in a seismic area of Southern Italy. Il Nuovo Cimento 6:609–629
• 4. Dobrovolsky IP, Zubkov SI, Miachkin VI (1979) Estimation of the size of earthquake preparation zones. Pure Appl Geophys 117:1025–1044
• 5. Fleischer RL (1997) Radon in earthquake prediction: radon measurements by etched track detectors: applications in radiation protection. In: Durrani SA, Ilic R (eds) Earth sciences and the environment. World Scientific, Singapore, pp 285–299
• 6. Grammakov AG (1936) On the influence of some factors in the spreading of radioactive emanations under natural conditions. Zh Geofiz 6:123–148
• 7. Negarestani A, Setayeshi S, Ghannadi-Maragheh M, Akashe B (2001) Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction. J Environ Radioact 62:225–233
• 8. Steinitz G, Begin ZB, Gazit-Yaari N (2003) Statistically significant relation between radon flux and weak earthquakes in the Dead Sea rift valley. Geology 31:505–508
• 9. Witten IH, Frank E (1999) Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco
• 10. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecasting 14:35–62
• 11. Zmazek B, Todorovski L, Džeroski S, Vaupotič J, Kobal I (2003) Application of decision trees to the analysis of soil radon data for earthquake prediction. Appl Radiat Isot 58;6:697–706
• 12.Zmazek B, Živčić M, Vaupotič J, Bidovec M, Poljak M, Kobal I (2002) Soil radon monitoring in the Krško basin, Slovenia. Appl Radiat Isot 56:649–657