Development of IDF Relations for Thailand in Consideration of the Scale-Invariance Properties of Extreme Rainfall Processes
Bibliographic record
Abstract
Intensity-duration-frequency (IDF) relations of extreme rainfalls at a single location are usually required for planning and design of urban and highway drainage structures. Traditionally, these IDF relations were developed based on the frequency analysis of annual extreme rainfalls in which a common probability distribution such as the generalized extreme value (GEV) was fitted to the annual extreme rainfall data for different durations without considering the dependence between these extreme rainfalls. This traditional approach could lead to inaccurate estimation of extreme rainfalls for different return periods. In the present study, an improved procedure was proposed to describe the distribution of extreme rainfalls in consideration of the scale-invariance properties of extreme rainfall processes for different durations. More specifically, the proposed approach was based on the use of the GEV distribution and on the identification of the scaling behavior of the extreme rainfall processes for different time scales. The feasibility of this scaling GEV method was tested using annual extreme rainfall data from a network of nine raingage stations located in the north and northeast region of Thailand during 1950-2010. Results of this illustrative application have indicated the feasibility and accuracy of the proposed scale-invariance GEV model in the derivation of the IDF relations. In addition, the annual extreme rainfall data for this study region were found to display a simple scaling behavior over different time scales.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".