Evaluating Polynomial, and Gaussian Approaches for Temperature Change Sub Index of Water Quality Index for Smart Environmental Management
Bibliographic record
Abstract
Water temperature plays a crucial role in aquatic ecosystem health, affecting oxygen solubility, organism metabolism, and overall water quality.This study evaluates Linear Interpolation, Polynomial, and Gaussian Models for predicting the Temperature Change Sub-Index in a Water Quality Index (WQI).Using a dataset from NSF-WQI, we propose a new formula based on polynomial and Gaussian approaches, offering improved accuracy over the traditional linear interpolation method for better water quality assessment.The Gaussian Model 1 emerged as the most accurate, with a 97.54% accuracy and a mean error of 2.46, outperforming the Cubic Polynomial and Fourth-Degree Polynomial Models, which achieved accuracies of 94.60% and 96.03%, respectively.The Gaussian Model's ability to capture peak characteristics and symmetrical declines makes it particularly effective for applications requiring precise water quality predictions.The integration of Gaussian Model 1 into IoT-enabled realtime monitoring systems presents significant potential, enabling continuous, accurate predictions critical for managing sensitive aquatic environments.However, the study acknowledges limitations, including the narrow data range that may limit Model generalizability and the complexity of higher-degree polynomial models, which could reduce practical applicability.Future research should focus on validating these Models across more diverse datasets, exploring hybrid Models that combine Gaussian and polynomial strengths, and enhancing computational efficiency to support broader realtime applications.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".