Beyond conventional modeling: A cutting-edge hybrid IAER-AMT decision-tree-based algorithm for high-resolution river turbidity prediction
Bibliographic record
Abstract
Water Turbidity (TU) is a widely used indicator of water quality. Given the time-consuming nature of direct TU measurement, developing an accurate predictive model is imperative. In this study, the alternating model tree (AMT) and its ensemble version through iterative absolute error regression (IAER), bootstrap aggregating (BA), weighted instance handler wrapper (WIHW), and random subspace (RS) were used to predict TU at Clackamas River, USA. Daily time-series data of the physicochemical water quality variables from 2006 to 2023, including water temperature (Tw), specific conductance (SC), dissolved oxygen (DO), pH, as well as physical river parameters, including daily water discharge (Q) and water stage (WS), were used as potential input variables to predict TU. The manual approach, principal component analysis (PCA), and correlation-based feature selection subset evaluation (CfsSubsetEval) techniques were compared under different input scenarios. Finally, the performance of the models was evaluated using various statistical metrics, including the coefficient of determination (R 2 ), root mean squared error (RMSE), percentage of bias (PBIAS), Nash-Sutcliffe efficiency (NSE), and root mean standard deviation ratio (RSR). WS had the highest impact on TU prediction, whereas Tw was less correlated. In addition, the input scenario that included all variables led to the highest model performance. Based on the testing dataset, the novel IAER-AMT hybrid algorithm outperformed others, achieving an RMSE of 1.20 Formazin Nephelometric Units (FNU), an NSE of 0.72, a PBIAS of 3.17 %, and an RSR of 0.53 followed by BA-AMT (RMSE = 1.30 FNU, NSE = 0.67, PBIAS = −9.73%, and RSR = 0.57), WIHW-AMT (RMSE = 1.34 FNU, NSE = 0.65, PBIAS = −0.35%, RSR = 0.58), RS-AMT (RMSE = 1.37 FNU, NSE = 0.64, PBIAS = −20.95%, and RSR = 0.60), and AMT (RMSE = 1.38 FNU, NSE = 0.63, PBIAS = −26.81%, and RSR = 0.60).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".