Papers for

hydraulic engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Knowledge graph improves water level forecasts in hydraulic models

Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting

Abstract: We investigate whether coupling a time-series foundation model to hydraulic project knowledge improves surrogate forecasting of HEC-RAS water-surface elevation (WSE). We present KG-Chronos-2, which combines a frozen Chronos-2 predictor with exact-state residual decoding, graph-conditioned historical retrieval, and input-aligned correction. We compare the method with persistence, a residual LSTM, project-conditioned recurrent GeoFNO, a hydraulic DCRNN-style model, and frozen Chronos-2. Task-specific fitting uses the 2008 simulation. Evaluation covers 64 fixed 24-hour windows from the 2011 and 2002 simulations at 4,675 cross sections in 71 reaches on a shared geometry. KG-Chronos-2 achieves event-balanced root-mean-square error 0.246970 in native WSE units. It reduces RMSE by 14.13% relative to frozen Chronos-2, 29.38% relative to the hydraulic DCRNN-style model, and 39.54% relative to recurrent GeoFNO. The 95% hierarchical-bootstrap interval for its event-balanced RMSE difference from frozen Chronos-2 is [-0.075177, -0.016317]. KG-Chronos-2 also achieves the lowest active-window and final-lead RMSE among the six completed systems. These results support coupling a frozen temporal predictor to project knowledge for warm-start HEC-RAS forecasting on the fixed benchmark.

Fri 18 SeptMachine LearningArtificial Intelligence
The gist
Predicting water levels in rivers and streams is important for managing floods and water resources. The authors show that linking a time-based prediction tool with information about the river system itself helps make better forecasts. Their new method, called KG-Chronos-2, outperforms other models when tested on past simulated river data. This approach helps start predictions more accurately by combining what happened before with detailed knowledge about the river.
Open → 2609.21381v1