A large sample study of the effects of upstream hydrometeorological input features for LSTM-based daily flow forecasting in Canadian catchments
Bibliographic record
Abstract
Recent years have seen an increase of deep learning applications for flow forecasting. Large-sample hydrological (LSH) studies typically try to predict the runoff of a catchment using some selection of hydrometeorological features from the respective catchment. One aspect of these models that has received little attention in LSH is the effect that data from upstream catchments has on model performance. The number of available and stations and distance between stations is highly variable between catchments, which creates a unique modelling challenge. Existing LSH studies either use some form of linear aggregation of upstream flows as input features or omit them altogether. The potential of upstream data to improve the performance of real-time flow forecasts has not yet been systematically evaluated on a large scale. The objective of our study is to evaluate methods for integrating upstream features for real-time, data-driven flow forecasting models. Our study uses a subset of Canadian catchments (n>150) from the HYSETS database. For each catchment, long-short term memory networks (LSTMs) are used to generate flow forecasts for lead times of 1 to 3 days. We evaluate methods for identifying, selecting, and integrating relevant upstream input features within a deep-learning modelling framework, which include using neighbouring upstream stations, using all upstream stations, and using all stations with embedded dimensionality reduction. Early results indicate that while the inclusion of upstream data often yields improvements in model performance, including too much upstream information can easily have detrimental effects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".