Application of adaptive neuro-fuzzy inference system for the estimation of roughness coefficient of a meandering open-channel flow
Bibliographic record
Abstract
An experimental investigation concerning the variation in roughness for meandering channels with flow depths, aspect ratio and sinuosity is presented.Test results revealed that the value of roughness coefficient in terms of Chezy's C increases with increase in aspect ratio and sinuosity.Adaptive neuro-fuzzy-based inference system (ANFIS), an integrated system, a combination of fuzzy logic and neural network is employed to find out the roughness coefficient of a meandering channel.Estimation of roughness coefficient is important for forecasting of discharge because its flexibility to resolve issues supported nonlinearity, randomness and uncertainty of knowledge.In the present work, an ANFIS-based model is developed for the prediction of the roughness coefficient of a meandering channel in terms of Chezy's C. Different standard methods to predict this variable are conjointly tested and verified with the laboratory findings as well as global data moreover.By comparing the results with the established standard methods available in the literature, it was observed that traditional methods could not provide satisfactory output at different surface and hydraulic conditions.Statistical error analysis is also carried out in which it was found that ANFIS model performed more accurately giving results with less error than different existing strategies.The analysis shows a high level of accuracy with regard to the ANFIS-based model developed for predicting the Chezy's C especially coefficients of determination are found to be more encouraging.
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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) | 0.000 | 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 | 0.000 | 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 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".