Comparing deep-learning and semi-distributed models for flow forecasting at fine spatial and temporal resolutions: a case study of 40 urbanized catchments in Toronto, Canada.
Bibliographic record
Abstract
Flood early warning systems rely on accurate streamflow forecasts. Deep learning based approaches have been widely shown to outperform traditional, process-based approaches. While literature is rich with comparisons between these opposing modelling paradigms, most comparisons have been conducted at daily temporal resolutions and feature spatially coarse (i.e., lumped) process-based models. Flood forecasting applications, especially those in flashy urban catchments, rely on sub-daily forecasts. In this work, we compare the performance of a state-of-the-art regionally trained LSTM models with semi-distributed StormWater Management Models (SWMM) at temporal frequencies ranging from 15-minutes to 1-day, for roughly 40 highly urbanised catchments in Toronto, Canada. Results show that the LSTM approaches struggle at fine temporal resolution and when limited observed data is available. In contrast, SWMM models can be automatically parameterized and calibrated using comparatively much less data. While the amount of available historical data would be enough to train deep learning models at a daily resolution, it is insufficient to train hourly models, which we attribute to the comparatively more complex urban rainfall-runoff system. Potential solutions to this problem include model transfer between space and different temporal frequencies. Finally, another contribution of this work is LSTM hyperparameter optimization, which is not widely documented at a sub-hourly resolution. Results from this research reaffirm the need for multi-model approaches for flood forecasting, particularly in urbanised catchments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".