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.
Topology enables learning-based hydrodynamic prediction of the global river system
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arXiv Machine Learning
Jun 11
LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data
arXiv:2606. 11268v1 Announce Type: new Abstract: Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs.
Hugging Face Trending Papers
Sep 10
A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph
The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years, combining satellite altimetry and in‑situ gauge data to enable large‑scale spatiotemporal graph imputation. The authors highlight the extreme sparsity of observations—less than 1% of sections per day—and the directed acyclic topology of river networks, which challenge existing imputation methods. They propose a bidirectional selective state‑space model that samples connected subgraphs and uses topology‑aware positional encodings, achieving 18–39% lower RMSE than the current state‑of‑the‑art SWOT‑based approach while providing predictions for all river sections.
arXiv Machine Learning
Jun 18
INDEQS: Informed Neural controlled Differential EQuationS
arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.
arXiv Machine Learning
Jul 7
MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs
arXiv:2607. 05167v1 Announce Type: new Abstract: Many real-world systems are organized as networks where spatio-temporal dynamics unfold along connections and not discretely between nodes.
Hugging Face Trending Papers
Aug 17
OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation
Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs,...
arXiv Machine Learning
Jun 30
CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
arXiv:2510. 09484v3 Announce Type: replace Abstract: Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models.
arXiv Machine Learning
Sep 11
A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph
The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years (2016‑2026) that integrates satellite altimetry, including SWOT, to enable large‑scale spatiotemporal graph imputation. The dataset is extremely sparse—fewer than 1% of sections are observed daily—and features a directed acyclic river topology that is larger and structurally distinct from existing benchmarks. The authors demonstrate that conventional imputation methods struggle with this topology, scale, and sparsity, and propose a bidirectional selective state‑space model that outperforms prior approaches, reducing RMSE against in‑situ gauges by 18‑39% and providing predictions for every river section. whyItMatters":"AmazonSWE offers a novel, real‑world use case that could improve flood forecasting and water resource management by enabling more accurate and comprehensive water surface elevation estimates across a vast, sparsely monitored river network."
arXiv AI
Aug 18
OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation
arXiv:2608. 16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning.
arXiv AI
Jun 3
Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins
arXiv:2606. 02791v1 Announce Type: new Abstract: Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological processes.
arXiv AI
Jul 28
AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.
arXiv Machine Learning
Aug 4
Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems
arXiv:2608. 01775v1 Announce Type: new Abstract: Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations.