Bibliographic record
Abstract
Abstract. An increasing number of studies have shown the prowess of Long Short-Term Memory (LSTM) networks for hydrological modelling and forecasting. One commonly cited drawback of these methods, however, is the requirement for large amounts of training data to properly reproduce streamflow events. For maximum annual streamflow, this can be problematic since they are by definition less common than mid- or low-flows, leading to under-representation in the model’s training set and, ultimately, parameterization. This study investigates six methods to improve peak streamflow simulation skill of LSTM models used to extend streamflow observation time series for flood frequency analysis (FFA). Methods include adding meteorological data variables, providing streamflow simulations from a distributed hydrological model, oversampling peak streamflow events, adding multihead attention mechanisms, adding data from a large set of “donor” catchments and combining some of these elements in a single model. Furthermore, results are compared to those obtained by the distributed hydrological model HYDROTEL. The study is performed on 88 catchments in the province of Quebec using a leave-one-out cross-validation implementation and an FFA is applied using observations as well as model simulations. Results show that LSTM-based models are able to simulate peak streamflow as well (for a simple LSTM model implementation) or better (with hybrid LSTM-hydrological model implementations) than the distributed hydrological model. Multiple pathways forward to further improve the LSTM-based model’s ability to predict peak streamflow are provided and discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.021 | 0.010 |
| Insufficient payload (model declined to judge) | 0.310 | 0.237 |
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".