Modeling total dissolved gas supersaturation in high dam reservoir using advanced novel bidirectional best-first disjoint aggregating M5-rule based algorithms: An innovative framework for ungauged regions
Bibliographic record
Abstract
• TDG predicted by novel IAER-M5-Rule, DA-M5-Rule, RS-M5-Rule, and WIHW-M5-Rule models. • A new bidirectional best-first algorithm was employed for feature selection. • Models were developed in one station, while their generalization power was tested by another station. • All input variables except discharge were effective on TDG prediction. • The DA-M5-Rule model outperforms other models during the testing and validation stages. Total Dissolved Gas (TDG) supersaturation is a critical ecological indicator for assessing water quality downstream of high dam reservoirs. TDG concentration exceeding 110 % can cause gas-bubble trauma in fish, leading to mortality and adversely affecting other aquatic organisms. We used hourly datasets of Water Temperature, Barometric Pressure, Spill from Dam, Sensor Depth, and Discharge to calibrate the Bidirectional Best First (BBF) algorithm for feature selection and ensemble-based models (hybrid of Iterative Absolute Error Regression (IAER), Disjoint Aggregating (DA), Random Subspace (RS), and Weighted Instance Handler Wrapper (WIHW) with M5-Rule) for TDG prediction. The TDG prediction capabilities of these models were compared to those of the Support Vector Regression model using statistical metrics. The study determined that Spill from Dam significantly influences TDG prediction at both stations (30.3 % and 31.9 %), while Barometric Pressure is the least effective variable (13.9 % and 15.6 %. According to the BBF technique, an optimal input scenario consists of Spill from Dam, Water Temperature, Barometric Pressure, and sensor depth. The DA-M5-Rule model with root mean square error (RMSE) = 4.00 %, and uncertainty coefficient with 95 % confidence level (U 95% ) = 11.00 had the highest predictive power followed by IAER-M5-Rule (RMSE=4.41 %, U 95% =12.16), WIHW-M5-Rule (RMSE=4.42 %, U 95% =12.17), RS-M5-Rule (RMSE=4.61 %, U 95% =12.23) and SVR (RMSE=5.71 %, U 95% =15.00) at the testing stage, while for generalization stage DA-M5-Rule model (RMSE=4.21 %, U 95% =11.08) had the highest generalization power, followed by RS-M5-Rule (RMSE=4.22 %, U 95% =10.90), IAER-M5-Rule (RMSE=4.30 %, U 95% =11.40), WIHW-M5-Rule (RMSE=4.36 %, U 95% =11.44), and SVR (RMSE=5.00 %, U 95% =13.50). The relative deviation of the developed models varied between 0.20–0.78 % during the testing stage and 1.72–2.00 % during the validation stage. Developed models in the current study can be applied as a promising tool across the USA. In addition, these models can be used to precisely predict TDG worldwide, particularly after evaluating its generalization power beyond the USA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".