Estimation of discharge capacity in meandering compound channels using artificial neural networks
Bibliographic record
Abstract
Flow in compound (or two-stage) channels is very complex and different energy loss mechanisms operate under different geometric and flow conditions. Neither theoretical analyses nor current empirical approaches are sufficiently developed for practical calculation of conveyance for all conditions experienced in practice. An alternative approach, using artificial neural network modelling, has been successfully applied to predict conveyance under a wide range of conditions. The model proposed uses a feed-forward system with one hidden layer and an error back-propagation learning procedure. It predicts a dimensionless discharge using input describing the main channel and floodplain flow depths, vegetation density over the cross section, channel sinuosity, transverse floodplain slope, and floodplain bend tightness. The discharge is dimensionalized by multiplication with the composite discharge calculated assuming frictional resistance only. The model was trained using 45 data sets representing a range of main channel and floodplain characteristics and tested using 15 additional data sets. The discharge prediction error for all the data used in development and testing the model was -0.19% on average and exceeded 15% for one condition only.Key words: compound channels, channel conveyance, flow resistance, neural networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".