Model Selection Tools for Hydrological Frequency Analysis: Some New Results
Bibliographic record
Abstract
Fitting probability distributions to data is important in hydrology, where one seeks to select a distribution that suits an observed data set well. Several distributions are available, some of which fit the data more closely than others. There is a need for comparing methods of discrimination between competing models, which differ in their ability to select the correct distribution from a group of alternatives. It is useful to compare the various methods, with a focus on discrimination between commonly used hydrological frequency models. We aim to address this practical problem. We will suggest some discrimination statistics (DS) and give recommendations concerning their ability for correct model selection. These DS include the ratio of maximized likelihood (RML) statistic, closely related to the akaike information criterion (AIC) and the bayesian information criterion (BIC). We will also include goodness-of-fit (GoF) statistics such as the Anderson-Darling DS, the “modified Shapiro-Wilk” DS, and the probability plot correlation coefficient DS. Despite the importance of 3-parameter distributions in hydrological practice, comparisons of discrimination tests between them have been limited. For this reason, our focus will be on 2-parameter models, to which many discrimination studies have been consecrated. Such models are frequently employed in the peaks-over-threshold (POT) approach to analyzing hydrological extremes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".