Geometry simulation of stable channel bank profile using evolutionary PSO algorithm implementation in an ANFIS model
Bibliographic record
Abstract
Forecasting the bank profile shape of stable hydraulic channels using empirical, experimental and numerical models is of special consideration among fluid mechanic and river science engineers. In the present paper, the application of soft computing methods is evaluated in predicting the geometry of stable channel cross sections. In this way, using a combination of the Particle Swarm Optimization (PSO) algorithm with an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, a novel evolutionary system called ANFIS-PSO is presented. The evolutionary model performance is assessed in comparison with a simple ANFIS model. The coordinates of points located on a channel boundary in stable state were also measured by the authors using a sensor instrument in the laboratory at 4 different flow discharge rates of 1.157, 2.18, 2.57 and 6.2 l/s. The results indicate that the evolutionary ANFIS-PSO model with Root Mean Squared Error (RMSE) and Mean Absolute Relative Error (MARE) of 0.0132 and 0.1326 performed better than the ANFIS model with 0.026 and 0.1426 error values respectively (almost 97% and 10% decrease in RMSE and MARE value for the ANFIS-PSO model, respectively). This demonstrates the high ANFIS-PSO model accuracy in predicting bank profile characteristics. The robust evolutionary ANFIS-PSO proposed can be used in designing and estimating stable channel dimensions. The second-degree polynomial equation proposed by the evolutionary ANFIS-PSO model can be utilized in predicting the coordinates of other points located on a stable boundary of a channel cross section.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".