Using GIS Combined with AHP for Mapping landslide Susceptibility in Mila, in Algeria
Bibliographic record
Abstract
Due to the complexity of its structure and morphology, the soil in the Mila area has experienced numerous landslides; damaging the road network and other supporting infrastructure.First, the most important landslide types were inventoried and mapped using existing data.The objective of this study is to develop a model based on the AHP analytical hierarchy process and integrate cartographic data into a GIS geographic information system for identifying and mapping regional landslide susceptibility.The approach uses factors such as slope, lithology, land use, road location, fault, flow and drainage network density as the main criteria to control the occurrence of selected landslides.The results showed that 15% of ground movement occurred in areas of high to very high susceptibility, 55% in areas of moderate susceptibility, and 30% in areas of very low to low susceptibility.The resulting map is subsequently validated by comparing the location of the mapped landslides with the susceptibility classes.The analysis of the results of this study shows that the landslide vulnerability map is a powerful decision support tool for local community development plans in the Algerian municipality of Mila.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".