Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting

• URL: http://arxiv.org/abs/2505.05381v1
• Date: Thu, 08 May 2025 16:13:41 GMT
• Title: Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting
• Authors: Kazi Ashik Islam, Zakaria Mehrab, Mahantesh Halappanavar, Henning Mortveit, Sridhar Katragadda, Jon Derek Loftis, Madhav Marathe,
• Abstract summary: DIFFFLOOD is a probabilistic forecasting method based on denoising diffusion models.<n>It predicts inundation level at a location by taking both spatial and temporal context into account.<n>We trained and tested DIFFFLOOD on coastal inundation data from the Eastern Shore of Virginia.
• Score: 2.6678519883651677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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