Identification Of A Suitable Probability Distribution For Rainfall Analysis Of Bankura District In West Bengal
Bibliographic record
Abstract
The daily rainfall data of 22 years (1992 to 1993 and 1997 to 2016) of Bankura district in West Bengal were collected from the Indian Meteorological Department (IMD), Kolkata. The data was then processed to identify the maximum rainfall received on any one day (24 hrs duration), in any week (7 days), in a month (4 weeks), in a monsoon season (4 months), pre-monsoon (4 months), post-monsoon(4 months) and in a year (365 days). The data were analysed to find out the standard deviation and coefficient of variation during all the six periods of study. The data showed that the annual daily maximum rainfall received at any time ranged between 58.7mm (minimum) to 258.8mm (maximum) indicating a large range of fluctuation during the period of study. These data were analysed using Anderson-Darling (AD) statistical goodness of fit teston the basis of probability plot to identify the best fit probability distribution for all the six period of study and the trend has been presented in this study. The lognormal distribution was found as the best fit probability distribution for the annual and monsoon period of study. The weibull (3P) was found as the best fit distribution for pre-monsoon and post-monsoon period of study. Weibull (3P), gamma (2P), lognormal, exponential and largest extreme value were observed in most of the weekly period of study as best fit probability distributions. The scientific results clearly established that the analytical procedure devised and tested in this study may be suitably applied for the identification of the best fit probability distribution of rainfall data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".