Efficacy of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models in Scour Depth Prediction at Submerged Weirs
Bibliographic record
Abstract
Accurate assessment of scour depth is crucial for the safe design of hydraulic structures, particularly around submerged weirs, where the scouring process is inherently complex due to intricate sediment transport and scour mechanisms. Although several studies have addressed scour at submerged weirs, the literature reveals limited investigations. Traditionally, scour depth prediction models have relied on regression analysis of laboratory data. However, recent advancements in soft computing techniques have shown superior predictive capabilities for complex modeling problems. This study uses soft computing models, specifically Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to predict scour depth at submerged weirs. These models are trained and validated using existing experimental data from the literature. Our analysis demonstrates that ANN and ANFIS outperform conventional regression models in predicting scour depth. The conventional Feed-Forward Backpropagation (FFBP) neural network yielded the highest predictive accuracy among the soft computing approaches. Additionally, sensitivity analysis identified flow intensity and relative weir height as the most influential parameters affecting relative scour depth. These findings underscore the potential of soft computing techniques in enhancing the reliability of scour depth predictions for hydraulic design applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".