Terrain-based mapping of landslide susceptibility using a geographical information system: a case study
Bibliographic record
Abstract
This paper deals with the development of a technique for mapping landslide susceptibility using a geographical information system (GIS), with particular reference to landslides on natural terrain. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. Landslide susceptibility in the study area is related to a number of terrain variables, viz., lithology, slope gradient, slope aspect, elevation, land cover, and distance to drainage line. Multiple correspondence analysis (MCA) was carried out to generate the principal axes that are linear combinations of these terrain variables using occurrence data of landslides and terrain variables. A GIS is used to project the values of the principal axes, and subsequently to relate these principal axes to landslide susceptibility by logistic regression modeling. The spatial landslide susceptibility response in the study area can then be obtained by applying this logistic regression model to the study area. The results from this study indicate that such a GIS-based model is useful and suitable for the scale adopted in this study.Key words: landslides, geographical information systems, multiple correspondence analysis, logistic regression, terrain analysis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".