10:15 AM - 10:30 AM
[HDS19-06] Maximum tsunami height prediction using pressure gauge data by a Gaussian process at Owase in the Kii Peninsula, Japan
Keywords: Tsunami height prediction, Gaussian Process, DONET
Previous works focused on the average of maximum absolute values of the hydrostatic pressure changes during a tsunami (Baba et al., 2013). Although compressing time series of pressure gauges data, they revealed a clear relationship between the average waveforms of DONET and tsunami heights at the coast. However, since they assumed linear relationship and used only the average of the data at all the DONET stations, it may be inadequate to take accurate tsunami prediction.
Here, using a standard nonlinear regression method, Gaussian process (GP), we construct an algorithm to predict maximum tsunami height. We found a greatly improved generalization error of the maximum tsunami height by our prediction model. The error is about one third of that by a previous method. Moreover, by optimizing each sensor’s weight of GP, we investigate the contributions of each ocean-bottom pressures on the predictions, which enables us to take more accurate prediction of tsunami height and could provide the design criteria of ocean-bottom sensors in the future.