Combining Tsunami Hazard and Vulnerability on the Assessment of Tsunami Inundation Probability in Taiwan
Bibliographic record
Abstract
As earthquake and tsunami are closely related, the probability of tsunami hazard had been done by extending the approach used in earthquakes. However, the hazard of tsunami depends also on the vulnerability of neighboring structures and hence its hazard and vulnerability should not be assessed separately. The distribution of tsunami height varies so significantly that the traditional definition parallel to that in seismic risk should be modified. Besides, previous studies on the probability of tsunami focused on the occurrence possibility of tsunami hazard in a fixed period of time, but this information is not applicable for a specific tsunami incidence. For the above-mentioned problems, a new algorithm that comprises two components is proposed in the present study. The first component, the Probabilistic Forecast of Tsunami Inundation (PFTI), is the conditional inundation probability once a tsunami of a specific height occurs, or an earthquake is detected at some specific location with a specific magnitude. PFTI comprises the assessments of both tsunami hazard and vulnerability, and can be directly applied to a specific tsunami incidence. The second component treats the Tsunamigenic Earthquake Number (TEN) modified from previous studies on tsunami hazard. These two components are combined to give the inundation possibility in a fixed period of time dubbed Earthquake-induced Tsunami Inundation Probability (ETIP) and the result can be used in urban planning or disaster mitigation guidelines. Application of this methodology to the coast of Taiwan is also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".