Predicting the geometry of regime rivers using M5 model tree, multivariate adaptive regression splines and least square support vector regression methods
Bibliographic record
Abstract
The complex dynamic equilibrium state of rivers, in which the amount of deposition and erosion are in balance, has been a fundamental research topic in river engineering. In this research, three advanced machine learning approaches, including M5 Model Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS) and Least Square Support Vector Regression (LSSVR), are employed to gain new insights and develop more accurate methods for assessment of the longitudinal slope (S), water-surface width (W) and mean water depth (D) of rivers in regime state. Geometric and hydraulic characteristic of 85 cross-sections of Gamasiab River (located in western of Iran), Kaaj River (located in southwestern of Iran) and Behesht-Abad River (located in southwestern of Iran) are used to train and evaluate the employed methods (M5Tree, MARS, and LSSVR). Seven different models comprising various combinations of effective parameters influencing regime river geometry (the flow discharge (Q), median bed grain size (d50) and Shields parameter (τ ∗)), are developed to evaluate the effect of each of these variables on the prediction of the geometry of regime rivers (S, W and D). The M5Tree method outperformed the other approaches with respect to correlation coefficient (R) values of 0.872, 0.951, and 0.770 and Mean Absolute Relative Error (MARE) values of 0.484, 0.102, and 0.126 for slope, width, and depth prediction, respectively. Furthermore, the flow discharge Q was the key variable governing regime channel width and depth while the regime channel slope was found to be mainly controlled by the Shields parameter.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".