arXiv AI
Jun 4
Physics-Informed Machine Learning for Short-Term Flood Prediction
arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.
Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
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arXiv AI
Aug 5
FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis
arXiv:2608. 03822v1 Announce Type: cross Abstract: Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation.
arXiv AI
Aug 24
MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater
arXiv:2512.12142v2 Announce Type: replace-cross Abstract: The Greenland ice sheet is melting at an accelerated rate due to processes that are not fully understood and hard to measure. The distributio...
arXiv Machine Learning
4d ago
A Model-Agnostic Physics-Guided Adapter for Few-Shot Transfer of Coastal Flood Prediction Models to Unseen Regions
arXiv:2609.37565v1 Announce Type: new Abstract: Deep learning surrogates can produce high-resolution coastal flood maps orders of magnitude faster than physics-based hydrodynamic simulators, yet tran...
arXiv AI
4d ago
Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction
arXiv:2603.25779v2 Announce Type: replace-cross Abstract: Groundwater represents a key element of the water cycle, yet it exhibits complex and context-dependent relationships that make its modeling c...
arXiv Machine Learning
Jun 9
Physics-Guided Dual Decoding and Spectral Supervision for Global 3D Hydrometeor Prediction
arXiv:2606. 08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables.
arXiv AI
Jun 12
Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video
arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.
arXiv Machine Learning
Jul 20
DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings
arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.
Hugging Face Trending Papers
Aug 4
FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis
Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation. Although generative models provide a promising means of data augmentation, existing methods often yield implausible spatial layouts of flooded regions and distort scene structures.
Hugging Face Trending Papers
Jun 23
Flood Mapping from RGB imagery using a Vision Foundation Model
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
arXiv AI
Jun 17
Remote sensing data imputation using deep learning for multispectral imagery
arXiv:2605. 24003v2 Announce Type: replace-cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years.
arXiv AI
Sep 23
Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale
arXiv:2609.22702v1 Announce Type: new Abstract: Accurate flood forecasts several days in advance are essential for flood control, water-resource management, and emergency response. Producing them at...