Data-driven modeling of sluice gate flows using a convolutional neural network
Bibliographic record
Abstract
Abstract Predicting the flow field around sluice gates is essential for controlling water levels and discharges in open channels and rivers. Smooth particle hydrodynamics (SPH) models can satisfactorily reproduce such free-surface flows, but they typically require long computational time and extensive computational resources. In this work, we propose a convolutional neural network (CNN) to predict the flow field around a sluice gate. A validated SPH model is used to carry out extensive simulations, and the generated data set is used to train and test CNN-based models. The results demonstrated that the developed CNN can accurately reproduce sluice gate flows, with R2 values exceeding 90% and significantly reducing the computational costs. Furthermore, various traditional machine learning algorithms comprising adaptive neuro-fuzzy inference system, genetic programing, multigene genetic programing, and one-dimensional CNN were also evaluated, and a comparison of the results showed that the developed CNN performed better than the traditional data-driven algorithms in predicting sluice gate flows. Therefore, the proposed method is a promising tool for providing rapid prediction of the spatial distribution of flow fields near the sluice, and potentially for predicting other spatially distributed hydrologic variables.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".