ClimateOpenAlex
Heuristic editor, no API keyVerdict: NotableHybrid Machine Learning for Predicting Particle Froude Number in Auto-washout Drainage Systems with Sedimented Beds
For proper utilization of the channel of the sewage system, an auto-washout method should be used to keep the deposited bed of the channel clean.
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Abstract
For proper utilization of the channel of the sewage system, an auto-washout method should be used to keep the deposited bed of the channel clean. Particle Froude number (PFr) is important in auto-washout methods. The prediction of PFr values becomes a very complex problem due to its multiple dependencies. In this study, five heterogeneous datasets collected from existing literature, covering a wide range of hydraulic and sediment conditions, were used to develop and validate the models. We used the Multilayer Perceptron Regressor (MLPR) as the base model and the Random Committee (RC-MLPR) as a hybrid machine learning (ML) model to predict the PFr value. Several performance measures were employed to assess the suggested models, including the agreement index, and other commonly used error criteria available in the published literature. The RC-MLPR model outperformed other proposed ML models, state-of-the-art ML models, and existing empirical equations. We also perform sensitive analysis that found the volumetric sediment concentration (Csed) is the most sensitive variable to predict PFr value by the hybrid RC-MLPR model. Received: 18 November 2025 | Revised: 24 June 2026 | Accepted: 25 August 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://doi.org/10.5281/zenodo.20591098. Author Contribution Statement Sanjit Kumar: Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Mayank Agarwal: Conceptualization, Validation, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision. Upaka Rathnayake: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 16% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ███░░ 3 | 20% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | ███░░ 3 | 20% | Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data. |
| Novelty | ██░░░ 2 | 10% | A new combination of known ideas. |
| Trajectory | ███░░ 3 | 14% | A clear path to scale. |
| Stakes | ██░░░ 2 | 20% | Benefits a professional community (practitioners, clinicians, engineers). |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (wide range, many tasks); gains (state of the art, outperforms); verification (multiple benchmarks).
How the score was computed
- Merit
- 5.4 / 10
- Adjusted merit
- 4.6 / 10
- Attention
- 0%
- Freshness
- 84%
- Citations0 (reference 15, via openalex, Sep 29, 2026, 23:38 UTC)
- Field-weighted citation impact0 (reference 3, via openalex, Sep 29, 2026, 23:38 UTC)