Evaluation of Australian Tsunami Warning Thresholds Using Inundation Modelling

Greenslade, Diana; Uslu, Burak; Allen, Stewart C. R.; Kain, Claire; Wilson, Kaya M.; Power, Hannah E.

doi:10.1007/s00024-019-02377-z

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…This has limited the application of these analytical approaches, prompting again the use of forward modeling and the basic principle of precomputed databases, albeit now aimed to estimate inundation. The less expensive propagation models from uniform slips sources are used to obtain tsunami time series at coastal sites, which become the input for table look-up procedures where tsunami inundation maps become the output 6 , 23 , 35 – 37 . Among these, the NearTIF algorithm 36 has been evaluated at several locations 38 – 41 with good results.…”

Section: Introductionmentioning

confidence: 99%

Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks

Núñez

Catalán

Valle

et al. 2022

Sci Rep

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Tsunamis are natural phenomena that, although occasional, can have large impacts on coastal environments and settlements, especially in terms of loss of life. An accurate, detailed and timely assessment of the hazard is essential as input for mitigation strategies both in the long term and during emergencies. This goal is compounded by the high computational cost of simulating an adequate number of scenarios to make robust assessments. To reduce this handicap, alternative methods could be used. Here, an enhanced method for estimating tsunami time series using a one-dimensional convolutional neural network model (1D CNN) is considered. While the use of deep learning for this problem is not new, most of existing research has focused on assessing the capability of a network to reproduce inundation metrics extrema. However, for the context of Tsunami Early Warning, it is equally relevant to assess whether the networks can accurately predict whether inundation would occur or not, and its time series if it does. Hence, a set of 6776 scenarios with magnitudes in the range $$M_w$$ M w 8.0–9.2 were used to design several 1D CNN models at two bays that have different hydrodynamic behavior, that would use as input inexpensive low-resolution numerical modeling of tsunami propagation to predict inundation time series at pinpoint locations. In addition, different configuration parameters were also analyzed to outline a methodology for model testing and design, that could be applied elsewhere. The results show that the network models are capable of reproducing inundation time series well, either for small or large flow depths, but also when no inundation was forecast, with minimal instances of false alarms or missed alarms. To further assess the performance, the model was tested with two past tsunamis and compared with actual inundation metrics. The results obtained are promising, and the proposed model could become a reliable alternative for the calculation of tsunami intensity measures in a faster than real time manner. This could complement existing early warning system, by means of an approximate and fast procedure that could allow simulating a larger number of scenarios within the always restricting time frame of tsunami emergencies.

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Section: Introductionmentioning

confidence: 99%

Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks

Núñez

Catalán

Valle

et al. 2022

Sci Rep

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“…Preparedness and timely early warning could mitigate losses for future events. There are a limited number of operational forecasting methodologies such as the ones used at the National Oceanic and Atmospheric Administration (NOAA) (Titov et al 2016), the Indonesian Meteorology, Climatology, and Geophysical Agency (BKGM) (Rudloff et al 2009), the Japan Meteorological Agency (JMA) (2013), and the Australia Tsunami Warning Systems (Greenslade et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A new tsunami runup predictor

2020

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We introduce a new parameter, tsunami runup predictor (TRP), relating the accelerating phase of the wave to the length of the beach slope over which the wave is travelling. We show the existence of a relationship between the TRP and the runup for different initial waveforms, i.e. leading elevation N-waves (LENs) and leading depression N-waves (LDNs). Then, we use the TRP to estimate tsunami runup for past tsunami events. The comparison of the runup estimates against field data gives promising results. Thus, the TRP provides first-order estimates of tsunami runup once the offshore waveform is known or estimated and, therefore, it could be beneficial to be implemented in tsunami early warning systems.

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