Exponential-gamma-Rayleigh distribution and its applications
Bibliographic record
Abstract
probability distribution help researcher and practitioners understand and model complex behaviour of rainfall data, ultimately behaviour of rainfall data and decision making in field of hydrology, water resource management and climate change impact assessment which intensify for specific duration simulation event and generate synthetic rainfall data and also optimize water resource management by modelling the probability of future rainfall scenarios Understanding and interpreting data behaviour more scientifically is an essential stage in every field of life. Statistical methods are used in applied in fields of hydrological, and mesosphere and lower thermosphere weather observations. Several researchers have generated new adaptable distributions from existing distributions using various modification techniques to increase their flexibility in rainfall modelling data. These adaptable distributions are created by adding extra parameters to the baseline distribution with generators or combining two distributions (Ali, et al., 2021). These modified distributions can model data sets efficiently and in most case, provide the best fit to data sets when applied because they have more parameters and are more adaptable than their baseline distributions. Data on the thirty observations for March rainfall in Minneapolis/St Paul (in inches), the data set has been used by Isa, et al., (2022), data sets obtained from Lee and Wang, (2003), and , the data set obtained from Fatima and Ahmad, (2017), which represents the 72 exceedances of flood maxima (in m3/s) of the Wheaton River near Carcoss in Yukon Territory, Canada, from 1958 to 1984 (rounded to one decimal point). The newly developed probability distributions robustness and versatility are evaluated by comparing them to other related existing probability distributions, such as the Exponential, Gamma, and Rayleigh distributions. Also, the Exponential-Gamma distribution developed by Ogunwale, et al., (2019), using goodness of fit measurements The Python 3.10.10 software package was used to analyse the data. The Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood function (l) are the goodness of fit measures discussed. The probability distribution with the lowest Akaike information criterion (AIC), Bayesian information criterion (BIC), or highest log-likelihood function (l) value will be used to determine the best-suited model.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".