Improved explicit formulation of bedload transport using a novel multi-level multi-model data-driven ensemble approach
Bibliographic record
Abstract
Abstract Estimation of bedload transport in rivers is a very complex and important river engineering challenge needs substantial additional efforts in pre-processing and ensemble modeling to derive the desired level of prediction accuracy. This paper aims to develop a new framework for the formulation of bedload transport in rivers using multi-level Multi-Model Ensemble (MME) approach to derive improved explicit formulations hybridized with multiple pre-processed-based models. Three pre-processing techniques of feature selection by Gamma Test (GT), dimension reduction by principal component analysis (PCA), and data clustering by subset selection of maximum dissimilarity (SSMD) are utilized at level 0. The multi-linear regression (MLR), MLR-PCA, artificial neural network (ANN), ANN-PCA, Gene expression programming (GEP), GEP-PCA, Group method of data handling (GMDH) and GMDH-PCA are used to develop individual explicit formulations at level 1, and the inferred formulas are hybridized with the MME approach at level 2 by Pareto optimality. A newly revised discrepancy ratio (RDR) for error distributions in conjunction with several statistical and graphical indicators were used to evaluate the strategy's performance. Results of MME showed that the proposed framework acted as an efficient tool in explicit equation induction for bedload transport (i.e., 33–96% reduction of RMSE; 2–29% increase of R2, 2-138% increase of NSE and 38–98% reduction of RAE in testing step in comparison with the best individual model) and clearly outperformed estimations made by other models. The current study highlights the importance of pre-processing and multi-modelling techniques in deep learning models to encounter the challenges of function finding for complex bedload transport estimations in multiple observed datasets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".