Quantifying the Effect of Additional Training Data When Using Machine Learning to Predict Streamflow in Ungauged Basins
Bibliographic record
Abstract
One of the greatest challenges in hydrology is in the accurate prediction of streamflow in ungauged basins. In recent years, machine learning models have made great strides in the ability to accurately predict these flows, but these models are extremely data intensive. However, resources for streamflow monitoring are often quite limited so it is important to gather data as efficiently as possible. In this study, we establish a relationship between the amount of data used in training and the quality of predictions in ungauged basins for Long-Short Term Memory-based neural network models. First, using the CAMELS dataset, we create numerous training sets with a different number of basins in each and corresponding testing sets that use basins outside of the training set to simulate ungauged basins. We then quantify how changing the size of the training sets and the length of the training data affects the quality of predictions in the testing sets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".