Real-Time Water Quality Assessment via IoT: Monitoring pH, TDS, Temperature, and Turbidity
Bibliographic record
Abstract
Water quality monitoring is crucial for detecting changes in aquatic resources. Traditional methods, which typically involve in-situ sample retrieval followed by laboratory assessments, have been perceived as laborious and time-consuming. Herein, a state-of-theart, open-source framework is introduced, leveraging the potent synergy of the Internet of Things (IoT) and cloud computing for real-time water quality evaluations. Commercially accessible sensors were utilized for the instantaneous acquisition and interpretation of essential water quality parameters: pH, temperature, total dissolved solids (TDS), and turbidity. Accuracies of 98.54%, 96.85%, and 98.10% were obtained for temperature, pH, and TDS measurements, respectively, based on chosen accuracy metrics. The resilience of the proposed system was ascertained through a comprehensive study at the Troso River, Indonesia. During this evaluation, 4,833 data entries were amassed within a two-hour period. Outcomes from this research, elucidated in the subsequent sections, underscore the proficiency of the system in real-time water quality surveillance. This investigation augments the extant literature, underscoring the transformative role of cloud computing in facilitating instantaneous raw data collection for water quality assessment endeavors.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".