Using artificial neural networks to estimate snow water equivalent from snow depth
Bibliographic record
Abstract
Snow water equivalent (SWE) is among the most important variables in the hydrological modelling of high latitude and mountainous areas. While manual snow surveys can directly provide SWE measurements, they are time consuming and costly, especially compared to automated snow depth measurements. Moreover, SWE is strongly correlated to snow depth. For this reason, several empirical equations relating snow depth to SWE have been proposed. The present study investigates the potential of artificial neural networks for estimating SWE from snow depth and commonly available data, and the proposed method is compared to existing, regression-based methods. An ensemble of multilayer perceptrons is constructed and trained using gridded meteorological variables and a data set of almost 40,000 SWE and depth measurements from the province of Quebec (eastern Canada). Overall, the proposed artificial neural network-based method reached a RMSE of 28 mm and outperforms by 17% a series of empirical equations for estimating the SWE of an independent set of measurement sites. Nevertheless, all the tested methods demonstrated limits to estimate lowest values of snow bulk density.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".