SEDIMENT MODEL FOR NATURAL AND MAN-MADE CHANNELS USING GENERAL REGRESSION NEURAL NETWORK
Bibliographic record
Abstract
This paper presents a new sediment transport model using general regression neural network (GRNN) that are applicable for both natural and man-made channels. GRNN is a supervised network that trains quickly sparse data sets. The network architecture responses very well with data that is spasmodic in nature than back propagation algorithm. Field data (499 data) extracted from rivers in Selangor, Perak and Kedah are used in the training and testing phases. The model is further tested using hydraulics and sediment data from rivers in the United States namely Sacremento, Atchafalaya, Colorado, Mississippi, Middle Loup, Mountain Creek, Niobrara, Saskatchewan, Oak Creek, Red, Rio Grande rivers and Chop Irrigation Canal. Four independent variables, namely, relative roughness on the bed (R/d 50 ), ratio of shear velocity and fall velocity (U * /W s ), ratio of shear velocity and average velocity (U * /V) and the Froude Number (V/√gy) are used as input variables in the input layer and the total sediment load Q T as the output variable. The proposed GRNN sediment model had accurately predicted 89% of the river data sets (local and foreign rivers) with 90% of the predicted values lie in the discrepancy ratio of 0.5 – 2.0. For the sake of illustrations, accuracy of the derived sediment transport model is also measured using smaller range of discrepancy ratios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".