# Topology enables learning-based hydrodynamic prediction of the global river system

arXiv: https://arxiv.org/abs/2602.22293 · HTML: https://mlsanity.com/paper/2602.22293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy](https://mlsanity.com/paper/2203.14827/) (2022)
- [The Merit of River Network Topology for Neural Flood Forecasting](https://mlsanity.com/paper/2405.19836/) (2024)
- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
- [Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting](https://mlsanity.com/paper/2410.05536/) (2024)

## Abstract (arXiv)

Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning has transformed Earth-system modeling, but a system-level advance for river prediction lags for lack of reliable data. Exploiting the connectivity and dissipative dynamics of rivers, we introduce GraphRiverCast, a neural model for global river systems that predicts daily multivariate hydrodynamics at every reach of a 0.25degnetwork with only sparse gauges and no initial state. It shows no intrinsic skill decay with lead time, outperforms leading global river models by 25% in accuracy, and robustly generalizes to ungauged reaches and finer resolutions. GraphRiverCast lifts learning-based river prediction from isolated basins to a unified global system and offers insights for machine learning in data-scarce Earth systems.