A gis spatial analysis model for landslide hazard mapping application in alpine area
Bibliographic record
Abstract
This research describes an application of an existing method for evaluating landslide susceptibility in alpine contest that may be considered a useful support in better land-use planning and risk management. In order to perform the method and improve it creating landslide maps of probability, we investigated the several conditioning factors that in general affected these morphological processes. Firstly, a landslide inventory was prepared using both in-depth analysis of historical records and aero-photos (or orthophotos) investigation. Secondarily, a set of conditioning factors which may affect slope movement and failure (particularly lithology, geomorphology, land use, slope angle and aspect) was considered. Then, the method involved the application of GIS techniques, specifically, spatial Data Analysis application. The thematic maps of conditioning factors overlapping together with the support of the raster calculator allowed the susceptibility map creation. The method was applied to the Germanasca Valley, a small basin in the Italian Western Alps. This easy to use method allows one to individuate various classes of susceptibility and to identify slope, lithology and geomorphology, driven by old landslide events as the main conditioning factors. Furthermore, the individuation of area susceptible to landslides verification is strictly related to risk and, as a consequence, this method permits specific zone to be selected for detailed engineering geology studies in land-use planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".