SPLASH: semi-empirical prediction of landslide-generated displacement wave run-up heights
Bibliographic record
Abstract
Abstract Displacement waves (or tsunamis) generated by sub-aerial landslides cause damage along shorelines over long distances, making run-up assessment a crucial component of landslide risk analysis. Although site-specific modelling provides important insight into the behaviour of potential waves, more general approaches using limited input parameters are necessary for preliminary assessments. We use a catalogue of landslide-generated displacement waves to develop semi-empirical relationships linking displacement wave run-up ( R in metres) to distance from landslide impact ( x in kilometres) and to landslide volume ( V in millions of cubic metres). For individual events, run-up decreases with distance according to power laws. Consideration of ten events demonstrates that run-up increases with landslide volume, also according to a power law. Combining these relationships gives the SPLASH equation: R = a V b x c , with best-fitted parameters a = 18.093, b = 0.57110 and c = −0.74189. The 95% prediction interval between the calculated and measured run-up values is 2.58, meaning that 5% of the measured run-up heights exceed the predicted value by a factor of 2.58 or more. This relatively large error is explained by local amplifications of wave height and run-up. Comparisons with other displacement wave models show that the SPLASH equation is a valuable tool for the first-stage preliminary hazard and risk assessment for unstable rock slopes above water bodies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".