# Real-time probabilistic tsunami forecasting via generative AI

arXiv: https://arxiv.org/abs/2608.04327 · HTML: https://mlsanity.com/paper/2608.04327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 51 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 1 of 1)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)

## Abstract (arXiv)

Explicit onshore tsunami inundation forecasting can improve public risk awareness, but deterministically predicted inundation boundaries under highly uncertain conditions, such as near-field tsunamis generated by megathrust earthquakes, may falsely imply safety outside the boundaries. Consequently, current warnings primarily target coastal tsunami height, not onshore inundation. Although machine learning enables instant inundation predictions, they remain deterministic, lacking uncertainty quantification. Here, we develop a probabilistic ensemble model based on a conditional diffusion model (a type of generative AI) that reconciles accuracy with calibration. Validated with the 2011 Tohoku-oki earthquake data, our model faithfully tracks the postearthquake uncertainty decreasing over time while accurately predicting inundation depth and extent. Our framework shows that generative AI can shift tsunami forecasting from determinism to probabilism, providing a foundation for next-generation early warning.