Flood Peak Equations Based on Partial Duration Series (PDS) Approach of Rainfall Data Selection in Lack-Data Agricultural Catchment
Bibliographic record
Abstract
The present work aims to develop a flood peak equation because of data limitations due to the impact of damage to the streamflow measuring instrument as a result of the 2015 floods at an agricultural catchment in Sulawesi, Indonesia. Hydrologic data for the period 2002-2014 obtained from two hydrologic stations and one hydrometric station were applied to establish the research variables. Three variables were determined using the frequency analysis approach: design rainfall generated from partial duration series (RDP) and annual maximum series (RDA) of daily rainfall data, and design streamflow predicted from annual maximum series of daily streamflow data (QDA). Four types of frequency distributions are tested to determine those variables, consisting of Normal, Log Normal, Log Pearson Type III and Gumbel distributions. The third distribution was selected for determining all the variables with the largest difference in c2 and D values based on Chi-squared and Kolmogorov–Smirnov tests respectively. The design streamflow equation that represents the peak of the flood was formulated by substituting the QDA with an equation generated from the regression analysis of those three variables. A streamflow peak equation was successfully developed as a function of RDP in the form of a power equation with excellent performance measured using Mean Absolute Error (MAE) and Correlation Coefficient (r). This equation could be applied in all of the catchments by accommodating the area's weighting factor of the sub-catchments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".