Pub Date : 2019-01-30DOI: 10.5772/INTECHOPEN.83492
T. Katona
The safety of nuclear power plants with respect of natural hazards can be ensured by adequate characterization of hazards and proven design solutions to cope with natural hazard effects. Design and severe accident management require characterization of very rare event. The events identified for the design basis and for the safety analysis are with annual probability 10 − 4 –10 − 5 and 10 − 7 , respectively. In this chapter, a brief insight into the actual issues of natural hazard safety of nuclear power plants and related scientific challenges is provided. The state of the art of ensuring safety of nuclear power plants with respect to natural hazard is briefly presented with focus on the preparedness to the accident sequences caused by rare natural phenomena. The safety relevance of different hazards and vulnerability of NPPs to different hazards are discussed. Specific attention is made to the non-predictable phenomena with sudden devastating effects like earthquakes and fault ruptures. Post-event conditions that affect the on-site and off-site accident management activities are also considered. The “specific-to-nuclear” aspects of the characterization of hazards are discussed. This is a great challenge for the sciences dealing with hazard characterization. The possibility for ensuring nuclear safety is demonstrated presenting cases when the nuclear power plants survived severe natural phenomena.
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Pub Date : 2019-01-09DOI: 10.5772/INTECHOPEN.83403
F. Plaza, Rodrigo F. Salas, O. Nicolis
Earthquakes represent one of the most destructive yet unpredictable natural disasters around the world, with a massive physical, psychological, and economi-cal impact in the population. Earthquake events are, in some cases, explained by some empirical laws such as Omori’s law, Bath’s law, and Gutenberg-Richter’s law. However, there is much to be studied yet; due to the high complexity associated with the process, nonlinear correlations among earthquake occurrences and also their occurrence depend on a multitude of variables that in most cases are yet unidentified. Therefore, having a better understanding on occurrence of each seismic event, and estimating the seismic hazard risk, would represent an invaluable tool for improving earthquake prediction. In that sense, this work consists in the implementation of a machine learning approach for assessing the earthquake risk in Chile, using information from 2012 to 2018. The results show a good performance of the deep neural network models for predicting future earthquake events.
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