Commenced in January 2007
Paper Count: 30465
Bayesian Networks for Earthquake Magnitude Classification in a Early Warning System
Abstract:During last decades, worldwide researchers dedicated efforts to develop machine-based seismic Early Warning systems, aiming at reducing the huge human losses and economic damages. The elaboration time of seismic waveforms is to be reduced in order to increase the time interval available for the activation of safety measures. This paper suggests a Data Mining model able to correctly and quickly estimate dangerousness of the running seismic event. Several thousand seismic recordings of Japanese and Italian earthquakes were analyzed and a model was obtained by means of a Bayesian Network (BN), which was tested just over the first recordings of seismic events in order to reduce the decision time and the test results were very satisfactory. The model was integrated within an Early Warning System prototype able to collect and elaborate data from a seismic sensor network, estimate the dangerousness of the running earthquake and take the decision of activating the warning promptly.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075024Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3136
 Bouckaert R.R., Bayesian Network Classifiers in Weka, The University of Waikato, September 1, 2004.
 Bouckaert R., Frank E., Hall M., Kirkby R., Reutemann P., Seewald A., Scuse D., WEKA Manual for Version 3-6-2, The University of Waikato, January 11, 2010.
 Elkan C., The Foundation of Cost-Sensitive Learning, Proceedings of the Seventeeth International Joint Conference on Artificial Intelligence, 2001.
 Gasparini P., Manfredi G., Zschau (Eds.), Earthquake Early Warning Systems, Springer, 2007.
 Han J., Kamber M., Data Mining. Concepts and Techniques, Morgan Kaufmann Publishers, 2001.
 Lancieri M., Zollo A., Simulated shaking maps for the 1980 Irpinia earthquake, Ms 6.9: Insights on the observed damage distribution, in Soil Dynamics and Earthquake Engineering 29, 1208-1219, 2009.
 Marketos G., Theodoridis Y., Kalogeras I.S., Seismological Data Warehousing and Mining: a survey, International Journal of Data Warehousing & Mining, 4(1), 1-16, 2008.
 Tan P-N, Steinbach M., Kumar V., Introduction to Data Mining, Pearson Addison Wesley, 2006.
 Witten H.I., Frank E., Data Mining: Practical Machine Learning Tools and Techniques, Elseiver, 2005.
 Zollo A., Iannaccone G., Convertito V., Elia L., Iervolino I., Lancieri M., Lomax A., Martino C., Satriano C., Weber E., Gasparini P., Earthquake Early Warning System in Southern Italy, in Encyclopedia of Complexity and System Science, Springer, 2395-2421, 2009.