Selecting IoT-Enabled Water Quality Index Parameters for Smart Environmental Management
Bibliographic record
Abstract
The monitoring of water quality is crucial for safeguarding ecosystem health and ensuring the safety of water resources.The Water Quality Index (WQI) has been developed as a tool to condense complex water quality data into a single, easily interpretable value.Standard WQI calculations typically incorporate parameters such as pH, temperature, dissolved oxygen (DO), turbidity, and total dissolved solids (TDS).This study provides a comprehensive review of existing literature, focusing on the application of physicochemical and biological sensors in water quality monitoring.The findings indicate that biological sensors, particularly those used for detecting contaminants such as Escherichia coli (E.coli), are often unsuitable for real-time monitoring due to inherent technical limitations.In contrast, the integration of Internet of Things (IoT) technologies significantly enhances the capability for real-time monitoring, enabling the prompt detection of variations in water quality.The study suggests that future research should prioritize the development of a WQI that incorporates the selected parameters identified in this research, ensuring that IoT-based water quality monitoring systems operate with greater efficiency and reliability.Such advancements are essential for supporting the sustainable management of water resources and enhancing environmental protection efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".