Modeling Extreme Rainfall Using the Principle of Maximum Entropy
Bibliographic record
Abstract
Abstract This research work analyzes extreme rainfall events in a mining-metallurgical region of North Greece, using available data from four meteorological stations recorded between 2006 and 2021. The Maximum Entropy method was applied to estimate the parameters of various probability distributions and identify the optimal fit for accurate risk prediction. Model evaluation was conducted using Akaike’s Information Criterion (AIC). Results indicate that the Generalized Extreme Value (GEV) distribution provides the best fit for modeling rainfall extremes. The entropy-based approach effectively captures the empirical probability distribution and demonstrates robust performance in modeling extreme rainfall across the stations. These findings highlight key periods of heightened rainfall activity, offering a valuable basis to increase the reliability of future risk assessment studies and offer valuable benefits for the mining site, such as optimum planning of mining-related activities, increased productivity, improved health and safety at the workplace, reduced probability for infrastructure damage, and development of an effective prevention strategy.
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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) | 0.000 | 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".