Coastal flooding poses significant risks to communities, necessitating fast and accurate forecasting methods to mitigate potential damage. To approach this problem, we present DIFF-FLOOD, a probabilistic spatiotemporal forecasting method designed based on denoising diffusion models. DIFF-FLOOD predicts inundation level at a location by taking both spatial and temporal context into account. It utilizes inundation levels at neighboring locations and digital elevation data as spatial context. Inundation history from a context time window, together with additional co-variates are used as temporal context. Convolutional neural networks and cross-attention mechanism are then employed to capture the spatiotemporal dynamics in the data. We trained and tested DIFF-FLOOD on coastal inundation data from the Eastern Shore of Virginia, a region highly impacted by coastal flooding. Our results show that, DIFF-FLOOD outperforms existing forecasting methods in terms of prediction performance (6% to 64% improvement in terms of two performance metrics) and scalability.
View on arXiv@article{islam2025_2505.05381,
title={ Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting },
author={ Kazi Ashik Islam and Zakaria Mehrab and Mahantesh Halappanavar and Henning Mortveit and Sridhar Katragadda and Jon Derek Loftis and Madhav Marathe },
journal={arXiv preprint arXiv:2505.05381},
year={ 2025 }
}