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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.05381 |
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| _version_ | 1866912700227911680 |
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| author | Islam, Kazi Ashik Mehrab, Zakaria Halappanavar, Mahantesh Mortveit, Henning Katragadda, Sridhar Loftis, Jon Derek Hoops, Stefan Marathe, Madhav |
| author_facet | Islam, Kazi Ashik Mehrab, Zakaria Halappanavar, Mahantesh Mortveit, Henning Katragadda, Sridhar Loftis, Jon Derek Hoops, Stefan Marathe, Madhav |
| contents | Coastal flooding poses increasing threats to communities worldwide, necessitating accurate and hyper-local inundation forecasting for effective emergency response. However, real-world deployment of forecasting systems is often constrained by sparse sensor networks, where only a limited subset of locations may have sensors due to budget constraints. To approach this challenge, we present DIFF -SPARSE, a masked conditional diffusion model designed for probabilistic coastal inundation forecasting from sparse sensor observations. DIFF -SPARSE primarily utilizes the inundation history of a location and its neighboring locations from a context time window as spatiotemporal context. The fundamental challenge of spatiotemporal prediction based on sparse observations in the context window is addressed by introducing a novel masking strategy during training. Digital elevation data and temporal co-variates are utilized as additional spatial and temporal contexts, respectively. A convolutional neural network and a conditional UNet architecture with cross-attention mechanism are employed to capture the spatiotemporal dynamics in the data. We trained and tested DIFF -SPARSE on coastal inundation data from the Eastern Shore of Virginia and systematically assessed the performance of DIFF -SPARSE across different sparsity levels 0%, 50%, 95% missing observations. Our experiment results show that DIFF -SPARSE achieves upto 62% improvement in terms of two forecasting performance metrics compared to existing methods, at 95% sparsity level. Moreover, our ablation studies reveal that digital elevation data becomes more useful at high sparsity levels compared to temporal co-variates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations Islam, Kazi Ashik Mehrab, Zakaria Halappanavar, Mahantesh Mortveit, Henning Katragadda, Sridhar Loftis, Jon Derek Hoops, Stefan Marathe, Madhav Machine Learning Coastal flooding poses increasing threats to communities worldwide, necessitating accurate and hyper-local inundation forecasting for effective emergency response. However, real-world deployment of forecasting systems is often constrained by sparse sensor networks, where only a limited subset of locations may have sensors due to budget constraints. To approach this challenge, we present DIFF -SPARSE, a masked conditional diffusion model designed for probabilistic coastal inundation forecasting from sparse sensor observations. DIFF -SPARSE primarily utilizes the inundation history of a location and its neighboring locations from a context time window as spatiotemporal context. The fundamental challenge of spatiotemporal prediction based on sparse observations in the context window is addressed by introducing a novel masking strategy during training. Digital elevation data and temporal co-variates are utilized as additional spatial and temporal contexts, respectively. A convolutional neural network and a conditional UNet architecture with cross-attention mechanism are employed to capture the spatiotemporal dynamics in the data. We trained and tested DIFF -SPARSE on coastal inundation data from the Eastern Shore of Virginia and systematically assessed the performance of DIFF -SPARSE across different sparsity levels 0%, 50%, 95% missing observations. Our experiment results show that DIFF -SPARSE achieves upto 62% improvement in terms of two forecasting performance metrics compared to existing methods, at 95% sparsity level. Moreover, our ablation studies reveal that digital elevation data becomes more useful at high sparsity levels compared to temporal co-variates. |
| title | Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.05381 |