Assessment of Extreme Rainfall in Chiang Mai Utilizing the ACER Method
Bibliographic record
Abstract
Extreme rainfall is customarily utilized as fundamental data in the design of hydraulic structures.The precision of rainfall estimation corresponding to the return periods is crucial for the economic viability, structural integrity, and safety of such designs.Traditionally, a range of classical extreme value (EV) models are employed to facilitate the estimation of extreme rainfall events.The objective of the present study is to employ the average conditional exceedance rate (ACER) method for the purpose of estimating extreme rainfall patterns in Chiang Mai, Thailand.The ACER method is conventionally applied to derive extreme values from time series data and is typically employed in the field of ocean research.In this research, long-term rainfall data from 1999 to 2022 were collected from rainfall gauge stations managed by the Meteorological Department of Thailand.The results conclusively demonstrate that the ACER method yields highly consistent estimations of extreme rainfall.Consequently, these estimations hold significant relevance for informing the design and construction of hydraulic structures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".