abstractArticle history:Received 21

abstract
Article history:
Received 21 May 2015
Received in revised form 9 October 2015
Accepted 30 November 2015
Available online 23 December 2015
This study applied an extreme learning machine to produce rapid forecasts of tsunami waveforms in coastal areas
using tsunami signals recorded at specified locations. The remarkable training speed of the algorithm means that
it can run in real-time, and therefore it is suitable for early warning systems in near-field tsunami events.
Additionally, as a universal function approximator, the proposed method can capture nonlinearities exhibited
by the tsunami. Therefore, it provides advantages over the standard inversion analysis used in many existing
studies, which is typically developed under a linear assumption. We applied the proposed method to the 2011
Tohoku earthquake tsunami. Our results demonstrate that the proposed method is more accurate and does not
significantly increase the computing time, when compared with the standard method. Furthermore, our model
uncertainty analysis proves that the method is robust and reliable, despite its dependency on the random
input weights and biases (the forecasts from several consecutive runs showed insignificant variability).
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