Enhancing combined sewer flow prediction in data-limited urban areas using a semi-supervised learning framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Study region Combined sewer network outfalls spanning 16 ungauged urban areas in Algiers, the capital and the most populated city in Algeria. Study focus Precise real-time combined sewer flow (CSF) prediction is essential, particularly during extreme rainfall events and varying wastewater flow patterns. This study proposes a novel semi-supervised learning framework that enables knowledge refinement and transfer to enrich the database relevant for targeted data-driven modeling. The approach leverages self-organizing maps (SOM) for clustering flow patterns across different networks and under diverse operational conditions, with cluster-specific artificial neural networks (ANN) to provide the required predictive modeling for the given network node and operation instance. New hydrological insights We benchmark our proposed approach against standalone supervised models using real-world data from Algiers’ combined sewer network. At long-record sites, the framework delivered modest improvement (median KGE from 0.70 to 0.81 and R² from 0.74 to 0.76). However, at the short-record sites, where traditional ANN models often fail to deliver reliable forecasts, the framework demonstrated clear advantages, reducing RMSE by up to 30 % (median RMSE from 47 L/s to 34 L/s and KGE rose from 0.59 to 0.86). Our findings confirm the framework’s ability to deliver reliable predictions while offering interpretable hydrological insights, supporting scalable wastewater management, flood mitigation, and improved operational efficiency of wastewater treatment plants in developing cities with sparse monitoring infrastructure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it