Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models
Bibliographic record
Abstract
Deep learning (DL) 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 DL model. It aims to evaluate whether 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 operational challenges and enhances forecast skill, especially for 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 modelling 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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".