Generation Rainfall Intensity Equations for Intensity Duration Frequency (IDF) Curves (Case Study: Salah Al-Din, Iraq)
Bibliographic record
Abstract
The intensity of rainfall can be considered an important influence in designing and operating hydraulic structures.The intensity-duration-frequency (IDF) curves are very important for planning, managing, operating, and designing all water resource projects.The current study aimed to derive the curves of IDF and equations for the stations (Tikrit, Samraa, Baiji, and Tuz) in Salah Al-Din/Iraq.Using the maximum daily rainfall during the period between 1990 and 2022, developed the empirical equations for estimating rainfall intensity with the various rainfall durations and different return periods (IDF equations), by using three methods of frequency distribution techniques (Gumbel, Log Pearson III, and Log-Normal).By finding all the missing rainfall data using the homogeneity curve expectation maximization (EM) algorithm adopted in SPSS, and checking the consistency of the data using the double mass curve technique.The results showed that the rainfall intensity reduced as the duration increased, while rainfall of any duration showed a higher intensity if the return period of the rainfall was large.A comparison among the three distributions was made using the methods of testing goodness (Chi-Square, Anderson-Darling, and Kolomokorov-Simornov) using Easy Fit software 5.6.The test results proved that the Log Pearson III distribution was the best method for the study area having correlation coefficients 0.92, 1, 0.96, and 0.92 for Tikrit, Samraa, Baiji, and Tuz stations, respectively.Also, Bernard's equation with an error ratio of (α< 0.05), can be adopted as a general empirical equation for all hydraulic projects in the study area.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".