AI-driven morphoclimatic regional frequency modelling of sub-daily rainfall-extremes
Bibliographic record
Abstract
Common main limitations affect standard approaches to regional frequency analysis (RFA) of rainfall extremes. Our study focuses on three of them that are rather frequent: regional models address (a) a single duration, or (b) a single exceedance probability at a time, and/or (c) hold a small-to-medium homogeneous region only. We use unsupervised ensembles of artificial neural networks (ANNs) to set up four alternative RFA models of sub-daily rainfall extremes. These are fed with annual maximum series of rainfall depth of any length collected at 2238 raingauges in a large and climatically and morphologically heterogeneous region. Our models can predict parameters of a Gumbel distribution for any location within the study area and any duration in the 1–24 h range. Prediction is based on mean annual precipitation (MAP), or on twenty morphoclimatic covariates. Validation is performed over an independent set of 100 gauges, where locally fitted Gumbel distributions are used as reference. A common literature approach where Gumbel parameters are functions of MAP is used as benchmark. Our results show that multivariate ANNs remarkably improve the estimation of percentiles relative to the benchmark approach. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across time-aggregation intervals and space and can be adapted for considering more flexible target frequency distributions (e.g. 3-parameter models).
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".