Discharge estimation in converging and diverging compound open channels by using adaptive neuro-fuzzy inference system
Why this work is in the frame
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Bibliographic record
Abstract
The computation of total flow in a flooded river is very crucial work in designing economical flood defense schemes and drainage systems. Further, under non-uniform flow conditions like in converging and diverging compound channel, the traditional methods provide poor results with high errors. The analytical methods require the system of nonlinear equations to be solved, which is very complex. So, mathematical models that prompt in taking care of a complex system of problems are solved here through an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). By utilizing ANN and ANFIS, an attempt is made to predict the discharge in converging and diverging compound channels. In the analysis, the most influencing dimensionless parameters such as friction factor ratio, area ratio, hydraulic radius ratio, bed slope, width ratio, relative flow depth, angle of converging or diverging, relative longitudinal distance, and flow aspect ratio are taken into consideration for computation of discharge. Gamma test and M-test have been performed to achieve the best combinations of input parameters and training length respectively. The significant input parameters that influence the discharge are found to be friction factor ratio, hydraulic radius ratio, relative flow depth, and bed slope. A suitable performance is achieved by the ANFIS model as compared to ANN model with a high coefficient of determination of 0.86 and low root mean square error of 0.005 in predicting the discharge of non-prismatic compound channels taken under consideration.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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