An expert system for predicting the infiltration characteristics
Bibliographic record
Abstract
Abstract Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. This study includes a comparative analysis of the two machine learning techniques, M5P model tree (M5P) and Gene Expression Programming (GEP), in predictions of the infiltration characteristics. The models were trained and tested using the 7 combination (CMB1 – CMB7) of input parameters; moisture content (m), bulk density of soil (D), percentage of silt (SI), sand (SA) and clay (C), and time (t), with output parameters; cumulative infiltration (CI) and infiltration rate (IR). Results suggested that GEP has an edge over M5P to predict the IR and CI with R, RMSE and MAE values 0.9343, 15.9667 mm/hr & 8.7676 mm/hr, and 0.9586, 9.2522 mm and 7.7865 mm for IR and CI, respectively with CMB1. Although the M5P model also gave good results with R, RMSE and MAE values 0.9192, 14.1821 mm/hr, and 19.2497 mm/hr, and 0.8987, 11.2144 mm and 18.4328 mm for IR and CI, respectively, but lower than GEP. Furthermore, single-factor ANOVA and uncertainty analysis were used to show the significance of the predicted results and to find the most efficient soft computing techniques respectively.
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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) | 0.000 | 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) | 0.009 | 0.003 |
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".