MODELING THE CONDITIONAL PROBABILITY OF THE OCCURRENCES OF FUTURE LANDSLIDES IN A STUDY AREA CHARACTERIZED BY SPATIAL DATA
Bibliographic record
Abstract
The most crucial but difficult task in the analysis of the risk due to landslide hazard is the estimation of the conditional probability of the occurrence of future landslides in a study area within a specific time period given the presence of spatial and geomorphologic features. This contribution explores a modeling procedure for estimating that conditional probability. The procedure proposed consists of two steps. The first step is to divide the study area into a number of “prediction ” classes according to the hazard level for the likely occurrence of future landslides. “Favourability Functions ” based on the spatial and geomorphological data in the study area were used for the sub-division. The number of the classes is dependent on the quantity and quality of the input data. Each class represents a level of hazard with respect to the future landslides. We term it the “hazard-mapping step”. For this step, several quantitative models have been developed and the strategy is to reconstruct the typical settings in which the future landslides are likely to occur. The second step is to empirically estimate the conditional probability in each prediction class given the spatial and geomorphologic data based on cross- validation techniques. For the second step, termed the “probability estimation step ” the basic strategy of the cross-validation is to construct the prediction classes in the first step using the occurrences of the landslides from the first time-period and then to compare the prediction classes with the distribution of the landslide occurrences from the later time period. The statistics obtained from the comparison provides the crucial quantitative measure to estimate the conditional probability. We illustrate the modeling procedure using a case study, La Baie, Quebec in Canada. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".