Statistical analysis of the landslides triggered by the 2021 SW Chelgard earthquake (ML=6) using an automatic linear regression (LINEAR) and artificial neural network (ANN) model based on controlling parameters
Bibliographic record
Abstract
Abstract This study uses automatic linear regression (LINEAR) and artificial neural network (ANN) models to statistically analyze the area of landslides triggered by the 2021 SW Chelgard earthquake (ML = 6) based on controlling parameters. We recorded and mapped the number of 632 landslides into four groups (based on the Hungr et al. 2014): rock avalanche-rock fall, debris avalanche-flow, rock slump, and slide earth flow-soil slump using field observation, satellite images, and remote sensing method (before and after the earthquake). The results revealed that most landslides are related to debris avalanche-flow, rock avalanche, and slide earth flow under the disruption influence of slope structures in limestone and shale units and water absorption after the earthquake in young alluviums and terraces. The spatial distribution of landslides showed that the highest values of the landslide area percentage (LAP%) and of the landslide number density (LND, N/km2) occurred in the northern part of the fault on the hanging wall. The ANN models with R2 = 0.60-0.75 provided more accurate predictions of landslide area (LA, m2) than the LINEAR models, with R2 = 0.40-0.60 using multiple parameters. The elevation and slope were found to be the most influential parameters on the rock slump and the debris avalanche using ANN and LINEAR models. Aspect and elevation are the most important parameters for rock avalanches and rockfalls. The sliding earth flow and soil slump are most affected by the slope and elevation parameters. The peak ground acceleration (PGA) and the distance from the epicenter exhibited more effects on the LA than the intensity of Arias (Ia) and the distance from the rupture surface. Thus, the separation of seismic landslides using the classification of Hungr et al. (2014) can be helpful for predicting the LA more accurately and understanding the failure mechanism better.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".