Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas
Bibliographic record
Abstract
In cold-climate regions, road salt is used as a deicer for winter road maintenance. The applied road salt melts ice and snow on roads and can be washed off through storm sewer systems into nearby urban streams, harming the freshwater ecosystem. Therefore, aiming to develop a precise and accurate model to determine future chloride concentration in the Credit River in Ontario, Canada, the present work makes use of a “Graph Neural Network”–“Sample and Aggregate” (GNN-SAGE). The proposed GNN-SAGE is compared to other models, including a Deep Neural Network-based transformer (DNN-Transformer) and a benchmarking persistence model for a 6 h forecasting horizon. The proposed GNN-SAGE surpassed both the benchmarking persistence model and the DNN-Transformer model, achieving RMSE and R2 values of 51.16 ppb and 0.88, respectively. Additionally, a SHAP analysis provides insight into the variables that influence the model’s forecasting, showing the impact of the spatiotemporal neighboring data from the network and the seasonality variables on the model’s result. The GNN-SAGE model shows potential for use in the real-time forecasting of water quality in urban streams, aiding in the development of regulatory policies to protect vulnerable freshwater ecosystems in urban areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".