Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models
Bibliographic record
Abstract
Deep learning has recently shown promise for hydrological applications. This study investigates the accuracy of a hybrid multi-model neural network approach for streamflow forecasting, using nine conceptual hydrological models (eight lumped, one semi-distributed) and one deep learning (DL) model. It aims to evaluate if the Long Short-Term Memory (LSTM) DL model within the multi-model framework improves short-term streamflow forecasts over a Canadian catchment. By integrating traditional hydrological models with the LSTM, the study addresses the operational challenges and enhances the forecast skill, especially for the early lead-times up to 9 days. Results indicate that the combination of LSTM with other models leads to better performance, suggesting that DL achieves optimal results when paired with different modeling approaches. LSTM models appear to be promising predictive tools for hydrological forecasting when integrated within a hybrid multi-model framework, facilitating gradual adoption in operational settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".