Multi-Output Parameter Estimation of the Generalized Extreme Value Distribution for Flood Risk in Northeast Thailand
Bibliographic record
Abstract
This study proposes the improvement of an adaptive parameter estimation approach for the GEVD using multi-output machine learning for the non-stationary models and comparing it to maximum likelihood estimation for the stationary models. This method effectively estimates GEVD parameters, improving extreme value analysis. In order to forecast the return level of extreme rainfall in Northeast Thailand, which is a risk of flooding due to the huge amount of rainfall, the initial step is to identify the key variables that are used to estimate the three GEVD parameters: location, scale, and shape parameters ( $$\mu$$ , $$\sigma$$ , and $$\xi$$ ). All features can be accomplished by estimating the correlation coefficients and using them to calculate the parameters. The information was gathered from meteorological and satellite data in Northeast Thailand between 2012 and 2023. It includes various variables such as rainfall, climate, Normalized difference vegetation index (NDVI), and runoff. The data was compiled from the Meteorological Department of Thailand and 322 meteorological stations. The evaluation performance and accuracy of the model are compared. Finally, two-dimensional maps depicting return levels for various return periods (2, 5, 10, 20, 50, and 100 years) are being made available for future use. This study enhances parameter estimation for flood risk mitigation and water resource management.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".