Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins
Bibliographic record
Abstract
Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long Short-Term Memory (EA-LSTM) model, an architecture that distinctly processes static catchment attributes and dynamic meteorological forcings, trained without basin-specific calibration. We compile a cross-border dataset integrating daily meteorological forcings, static catchment attributes, and observed streamflow for 975 sub-basins across the United States and Canada (1980–2023). With a temporal training/testing split, the unified EA-LSTM attains a median Nash–Sutcliffe Efficiency (NSE) of 0.685 and a median Kling–Gupta Efficiency (KGE) of 0.678 in validation, substantially exceeding a standard LSTM (median NSE 0.567, KGE 0.555) and the operational NOAA National Water Model (median NSE 0.209, KGE 0.440). Although skill is reduced in the smallest basins (median NSE 0.554) and during high-flow events (median PBIAS −29.6%), the performance is robust across diverse hydroclimatic settings. These results demonstrate that a single, calibration-free deep learning model can provide accurate, scalable streamflow prediction across an international basin, offering a practical path toward unified forecasting for the Great Lakes and a transferable framework for other large, data-sparse watersheds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".