Harnessing IoT and advanced analytics for sustainable water quality management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Real-time IoT-enabled system improves water quality monitoring in the University of Ilorin. • Advanced analytics identify decreasing trends in Electrical Conductivity (EC) and Total Dissolved Solids (TDS). • Canadian Water Quality Index (CCME WQI) categorizes water quality as ’Excellent’ with a score of 94.42 %. • Principal Component Analysis (PCA) reveals EC and TDS as primary factors influencing water quality variability. • Solar-powered IoT system offers sustainable, continuous water quality monitoring for public health safety. Access to safe and clean water remains challenging in resource-constrained environments, where conventional laboratory-based assessments suffer from delayed feedback and limited sampling. This study developed and validated a solar-powered Internet of Things (IoT)–enabled real-time water quality monitoring framework, integrating sensor networks with cloud-based analytics. A weatherproof sensor system was deployed at the University of Ilorin Water Treatment Plant, collecting 100 time-stamped observations over 13 days. The system measured six key physicochemical parameters—pH, turbidity, temperature, oxidation–reduction potential (ORP), electrical conductivity (EC), and total dissolved solids (TDS). The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) yielded a score of 94.42 %, classifying the water as “ excellent ” . Pearson correlation revealed strong relationships between turbidity and temperature ( r = 0.59) and temperature and EC ( r = 0.43). Trend analysis using the Mann–Kendall test showed significant increases in turbidity ( τ = 0.339) and EC ( τ = 0.222), while pH declined ( τ = –0.383). Corresponding Sen’s slopes confirmed gradual daily changes. OLS regression supported turbidity’s upward trend ( β = 0.0261, R 2 = 0.202). Principal Component Analysis (PCA) reduced the dataset to three components, with the first two explaining 57.26 % of the total variance. PC1 was associated with TDS, EC, and turbidity; PC2 with pH and ORP; and PC3 with temperature. These results demonstrate the system’s capability for automated water quality assessment. Future work should expand temporal coverage, adopt predictive modeling, and extend deployments across multiple sites for broader impact.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it