Locating appropriate areas of municipal waste landfill using TOPSIS method (Case study: Langroud County)
Bibliographic record
Abstract
ABSTRACT Background & objective: The lack of suitable landfill for storage and disposal of solid wastes in different parts of Guilan has been not only led to deforestation and agricultural lands destruction, but also jeopardized public health. The Langroud County as a tourist destination of Guilan province is also affected by solid wastes. Therefore, the present study aims to locate the new appropriate area for municipal sanitary landfill, taking into account environmental standards in Langroud County. Materials & Methods: In this research, 20 criteria were used in accordance with the standards of the Iranian Environmental Protection Agency, the Alberta State Environmental Authority of Canada, the Minnesota Pollution Control Agency and the British Columbia Environmental Ministry as criteria for locating. TOPSIS method was used in combining the criteria maps in ArcGIS software environment. Results: After passing TOPSIS and combining the layers obtained in the ArcGIS software environment, the final map of urban landfill location within the study area was classified into five classes (very weak, weak, moderate, good and excellent). Conclusion: Based on the results, five prestigious areas in the south and southwest of the city with the highest degree of fit and excellent grade were proposed as new areas of urban solid waste disposal for the city of Langroud.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".