Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting
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Abstract (arXiv)
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.
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References · 12
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2020Knowledge Graphscited by 621
- 2022Fourier Neural Operator with Learned Deformations for PDEs on General Geometriescited by 427
- 2025Chronos-2: From Univariate to Universal Forecastingcited by 194
- 2024Retrieval Augmented Time Series Forecastingcited by 46
- 2024TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecastingcited by 17
- 2025Retrieval-Augmented Foundation Models for Water Level Prediction in the Evergladescited by 3
- 2025Physics-Informed Neural Network Surrogate Models for River Stage Predictioncited by 2
- 2025Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecastingcited by 2
- 2026Learned Response-Field Inertia Operator for HEC-RAS 2D Water-Surface Elevation Predictioncited by 1
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