A Comparison of Two Data-Driven Models to Predict Hypolimnetic Dissolved Oxygen Concentration: A Case Study of the Seymareh Reservoir in Iran
Bibliographic record
Abstract
Dissolved oxygen concentration (DO) is a crucial factor in maintaining aquatic ecosystem health. In this research, two data-driven modelling (DDM) techniques, multiple linear regression (MLR) and artificial neural networks (ANN), were developed, implemented and compared to predict the DO in the hypolimnetic layer of Seymareh Reservoir in Iran. Low DO in this Reservoir lead to a fish kill event and thus, this reservoir is of interest to water managers in the region. Water quality monitoring data from the Reservoir and an upstream river were used for training the models. In addition, two input variable selection methods, linear correlation analysis and combined neural pathway strength analysis (a nonlinear variable selection method) were developed and compared to determine the most significant inputs to predict hypolimnetic DO. A systematic method to select the optimum architecture of the network was also tested. These two approaches are typically ignored in DDM approaches and this research demonstrates the importance of using systematic input selection and network design for improved DO prediction in a large Reservoir. The performance of the models was quantified using the Nash-Sutcliffe efficiency and root mean squared error which demonstrated that the ANN approach had better performance compared to the MLR model. The approach demonstrates that by using a systematic input variable selection approach combined with an optimised network architecture, a high performance of DO prediction can be achieved using easily measured upstream input data.
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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 | 0.001 | 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) | 0.000 | 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 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".