Multi-criteria site selection for wastewater treatment plant in Bent Saidane, Tunisia
Bibliographic record
Abstract
Optimal site selection for the establishment of a municipal waste-water-treatment plant (WWTP) is an amazingly complex task that involves several factors such as regulations, as well as environmental, economic and social considerations. This paper presents the criteria and applied methods (geographic information system (GIS)/fuzzy analytical hierarchy process (AHP)) used in the screening of potential sites for the WWTP of the Bent Saidane rural locality (northern Tunisia). For this reason, nine factors accompanied by three sets of site selection criteria were used. GIS-based multi-criteria analysis techniques based on the standardised fuzzy logic concept and weighted overlay were applied for generation of all maps in the Universal Transverse Mercator coordinate system. Multiscale evaluations based on the weighted linear combination method were also assessed. The study indicated that 0.14 and 0.11% of the total area of the Bent Saidane locality were found to be highly suitable and very highly suitable, respectively. According to these results, the GIS followed by the fuzzy AHP method could pinpoint the optimal sites selected as the most suitable, due to their minimum impact on the environment, as well as on public health, for the construction of a future WWTP in the Bent Saidane rural area.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".