Neural networks modelling of streamflow, phosphorus, and suspended solids: application to the Canadian Boreal forest
Bibliographic record
Abstract
Sediment has long been identified as an important vector for the transport of nutrients and contaminants such as heavy metals and microorganisms. The respective nutrient loading to water bodies can potentially lead to dissolved oxygen depletion, cyanobacteria toxin production and ultimately eutrophication. This study proposed an artificial neural network (ANN) modelling algorithm that relies on low cost readily available meteorological data for simulating streamflow (Q), total suspended solids (TSS) concentration, and total phosphorus (TP) concentration. The models were applied to a 130-km2 watershed in the Canadian Boreal Plain. Our results demonstrated that through careful manipulation of time series analysis and rigorous optimization of ANN configuration, it is possible to simulate Q, TSS, and TP reasonably well. R2 values exceeding 0.89 were obtained for all modelled data cases. The proposed models can provide real time predictions of the modelled parameters, can answer questions related to the impact of climate change scenarios on water quantity and quality, and can be implemented in water resources management through Monte Carlo simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".