Optimal water quality monitoring network during road construction using Bayes and Entropy theories
Bibliographic record
Abstract
The spatial variation of road construction runoff, and environmental impacts on both the terrestrial and aquatic environment necessitate the monitoring of receiving water quality. The paper proposed an integrated methodology for spatial optimization of the water quality monitoring network (WQMN) using information-theoretic techniques, including the concepts of the Transinformation entropy (TE) and the value of information (VOI). First, based on the correlation analysis, the most significant water quality parameters were selected. Then, using the Canadian Council of Ministers of the Environment (CCME) method, the water quality index (WQI) was computed in each potential monitoring station. After that, the VOI and TE for all potential stations were calculated. To achieve an optimal network among potential stations, the NSGA-II multi-objective optimization model was developed considering three objective functions, including i) minimizing the number of stations, ii) maximizing the VOI in the selected network, and iii) minimizing TE by the selected nodes. The optimization model resulted in a set of optimal solutions for WQMNs, called Pareto-front. Finally, two multi-criteria decision-making models including TOPSIS and PROMETHEE were utilized for choosing the best solution on the Pareto-front space considering various weighing scenarios assigned to objectives. The applicability of the presented methodology was assessed in a WQMN of a road construction site (33 km) in E18 highway, south of Norway. The selected solutions by TOPSIS and PROMETHEE models present the WQMN with maximum VOI and minimum TE among 33, and 28 potential stations, respectively.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".