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

Abstract

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.

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

“…Authors then estimated the likelihood of earthquakes on the Indian subcontinent by looking at the CNN network [48]. The author in [49] investigated another CNN earthquake damage assessment model.…”

Prediction of Tsunami Alert Levels Using Deep Learning

“…Makinoshima et al (2021) use a convolutional neural network to predict the inundation time series at a given onshore point from synthetic tsunamis generated in the fault region of the 2011 Tohoku earthquake. Núñez et al (2022) use convolutional neural networks to predict tsunami time series at specific locations. Rim et al (2022) also use convolutional neural networks to forecast tsunami waveforms from Global Navigation Satellite System (GNSS) data at selected locations.…”

“…Núñez et al. (2022) use convolutional neural networks to predict tsunami time series at specific locations. Rim et al.…”

Multi-level emulation of tsunami simulations over Cilacap, South Java, Indonesia

“…Statistical emulation in tsunami risk assessment is a relatively unexplored field although Giles et al [12] and Gopinathan et al [11] have established an insightful framework in Gaussian Process (GP) tsunami emulation. Alternative approaches e.g., polynomial chaos and neural networks have been proposed [13][14][15]. More examples of GP emulation in tsunami simulation can be found in recent publications [16][17][18][19][20].…”