Copula Based Spatial Analysis of Drought Return Period in Southwest of Iran
Bibliographic record
Abstract
In the past years, Khuzestan province which is located in the southwest of Iran has experienced severe droughts. Drought can be explained by its characteristics known as duration or severity. However, combination of the two features by probabilistic approach is appeared to be a well improved method to describe the phenomena. The aim of this study is to provide a more accurate statistical method of determining drought based on simultaneous analysis of two drought characteristics. Here, precipitation data from twenty stations were used to determine drought characteristics, by Standardized Precipitation Index (SPI). Joint probability function of two variables were built via copula functions. The drought return period was calculated in the form of two scenarios. The first scenario is, based on an assumption that drought is recognized by at least one of the two specific characteristics. Drought in the second scenario is distinguished by the two characteristics in a joint probabilistic form. According to research results, there was no significant difference between the north and south of Khuzestan in the study of single characteristics of drought. While analyzing two characteristics of the drought, the return period in the north was shorter than the south. The return period of droughts in the east was always shorter than in the west. The drought return period varies from 30 to 52 months and 50 to 87 months for the first and second scenarios, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".