Comparing ELECTRE and Linear Assignment Methods in Zoning Shahroud-Bastam Watershed for Artificial Recharge of Groundwater with GIS Technique
Bibliographic record
Abstract
Today, uncontrolled exploitation of ground water has doubled water scarcity problem, while proper control and management of these resources can solve water shortage problem to some extent. One of the approaches for managing groundwater resources is artificial recharging of groundwater and determining the best location for this. This study aimed at ranking Shahroud–Bastam watershed using ELECTRE and linear assignment methods and the results of these two methods are compared. These two models are of multiple-criteria decision making compensation and coordinated subgroup models. The findings indicate that among the seven zones in ELECTRE method mentioned above, zones (3, 4,5) with four dominations and 2 defeats and 2 points are in the first ranking and are the most suitable zones for artificial recharge. Zone (1) with six defeats and no dominations and (-6) points is in the last ranking and is not suitable for artificial recharge of ground water. Zones (2, 6, 7) respectively with (2, 2, 1) dominations and (4, 4, 5) defeats and with (-2, -2, -4) points are in the next rankings respectively. Zones (1, 2, 6, 7) must be removed because the number of their defeats is more than the number of their dominations and have negative points. In linear assignment method, among 7 zones, zone 3 has the first rank and is the best zone for artificial recharge and zone 7 is in the last ranking and is not suitable for artificial recharge. Zones (4, 2, 5, 6, 1) are in the next rankings respectively. Between these two methods, the results of linear assignment method are more consistent with reality and are more accurate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".