Tsunamis in the last two decades have resulted in the loss of life of over 200,000 people and have caused billions of dollars in damage. There is therefore great motivation for the development and improvement of current tsunami warning systems. The work presented here represents advancements made towards the creation of a neural network-based tsunami warning system which can produce fast inundation forecasts with high accuracy. This was done by first improving the waveform resolution and accuracy of Tsunami Squares, an efficient cellular automata approach to wave simulation. It was then used to create a database of precomputed tsunamis in the event of a magnitude 9+ rupture of the Cascadia Subduction Zone, located only ∼100 km off the coast of Oregon, US. Our approach utilized a convolutional neural network which took wave height data from buoys as input and proved successful as maps of maximum inundation could be predicted for the town of Seaside, OR with a median error of ∼0.5 m.
Tsunami Squares is a computationally lightweight tsunami and inundation simulator which utilizes a unique cellular automata technique. We make modifications to the underlying algorithm which result in increased accuracy and enhanced waveform resolution. These improvements leave Tsunami Squares well suited for machine learning applications where large pre-computed tsunami simulation databases are required. Previous implementations relied heavily on a smoothing algorithm which acts as a moving average applied to the water surface heights and velocities to eliminate anomalies at every time step. Although this allowed the simulation to function properly, it brings several unwanted effects such as reduced wave detail and lowered energy. A solution is found by shifting the location at which the water surface gradient is calculated, reducing the amount of anomalies in the simulation, and thus lowering the amount of smoothing needed by a factor of $\sim 10$. Also introduced is a new method to conserve energy locally, compared to previous methods which reference a simulation-wide energy calculation. We make comparison tests were made using the 2011 Tohoku tsunami along with the 2010 Maule tsunami to demonstrate the improvements made.