arXiv:2606.06385v1 Announce Type: new Abstract: This article presents a cross-dataset evaluation of learned native-cell surrogate models for solver-consistent water-surface elevation (WSE) prediction in HEC-RAS 2D. To avoid raster remapping error and information-access confounding, surrogates are evaluated directly on the original nonuniform computational cells under an explicit policy that separates static project inputs, current hydraulic state, project-input forcing, calibration-derived quantities, and future solver-output targets. We introduce the Learned Response-Field Inertia Operator (L
The increasing availability of computational power and advanced AI techniques makes the development of sophisticated predictive models for complex environmental systems more feasible.
Accurate and efficient water-surface elevation prediction is crucial for disaster preparedness, infrastructure planning, and resource management, especially with changing climate patterns.
This research introduces a refined approach to hydrological modeling, potentially reducing prediction error and enhancing the reliability of flood and water resource forecasts.
- · Hydrological modeling firms
- · Urban planners
- · Disaster relief organizations
- · Insurance industry
- · Traditional hydraulic modeling reliance
- · Regions without access to advanced modeling
Improved flood forecasting and early warning systems could mitigate disaster impacts.
More precise water management could optimize agricultural output and reduce water-related conflicts.
Enhanced predictive capabilities may inform long-term climate adaptation strategies for vulnerable populations and infrastructure.
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Read at arXiv cs.LG