Neural network-based methodology for inter-arrival times of earthquakes

P. Kamatchi (), K. Balaji Rao, Nagesh Iyer and S. Arunachalam

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2012, vol. 64, issue 2, 1303 pages

Abstract: In this paper, an artificial neural network (ANN)–based methodology is proposed to determine the probability of inter-arrival time (IAT) of main shock of six broad seismic regions of India. Initially, classical methodology using exponential distribution is applied to IAT of earthquake events computed from earthquake catalog data. From the goodness-of-fit test results, it has been found that exponential distribution is not adequate. In this paper, a more efficient ANN-based methodology is proposed, and two ANN models are developed to determine the probability of IAT of earthquake events for a specified region, specified magnitude range or magnitude greater than the specified value. The performance of ANN models developed is validated with number of examples and found to predict the probability with minimal error compared to exponential distribution model. The methodology developed can be applied to any other region with the database of the respective regions. Copyright Springer Science+Business Media B.V. 2012

Keywords: Inter-arrival time; Artificial neural networks; India; Earthquakes; Probability (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11069-012-0297-z

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