Integration of the AHP Method and GIS Techniques for Mapping Areas Susceptible to Forest Fires in the Southern Amazon Region (Peru)
Bibliographic record
Abstract
In recent decades, the occurrence of forest fires has increased, causing damage to wild flora and fauna.For this reason, it is necessary to determine the areas susceptible to the occurrence of this phenomenon and thus implement policies for its management.In this study, the AHP and GIS method were used to map areas susceptible to forest fires in the province of Rodrí guez de Mendoza located in the southern Amazon region of Peru, using climatic variables (Temperature, Precipitation and Wind Speed), topographic (altitude, slope and aspect), socioeconomic (proximity to roads and distance to populated centers) and biological (NDVI).The results indicate that 23.65% of the area is in the high-risk class and 19.05% in the very high-risk class.These risk levels are directly related to the topographic, meteorological, social and biological variables, and could trigger large-scale fires, generating losses in biological diversity and economic losses.It is concluded that 42.70% of the study area is classified as high and very high-risk areas, which makes it necessary to take relevant measures to reduce the risk of natural disasters; Furthermore, the methodology used in this research can be used in other provinces that have similar conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".