Modeling of Surface Runoff Estimation in Tropical Palm Dates Plantations: A Case Study in Aceh Province, Indonesia
Bibliographic record
Abstract
One of the most popular surface runoff estimation methods is the rational method. Unfortunately, this method has several concentration-time approaches that have been developed, as one of the parameters, which are specific to the environment to increase the accuracy of the runoff estimation. Therefore, this study aims to estimate surface runoff using a rational method with several concentration-time approaches in order to obtain the best accuracy in tropical palm dates plantations in Aceh Province, Indonesia. The concentration-time approaches studied were Kerby, Kirpich, Manning, Bransby Williams, Federal Aviation Agency (FAA), and Natural Resources Conservation Service (NRCS). This research was conducted by making a test plot in the plantation with the length, width, and slope of 22 m, 4 m, and 25%, respectively. Each side of the test plot is given a barrier plate with a height of 15 cm and embedded as deep as 30 cm. In addition, on the bottom side, there is a runoff collection tank with a capacity of 50 L. The physical properties of the soil on the test plots in the form of structure, texture, porosity, permeability, and organic C were granular, sandy loam, 0.43%, 1.84 cm/day, and 1.25%, respectively. The test was carried out from March to November 2020 with 37 days of rain. The results of this study indicate that there are significant differences between each concentration-time approach being tested. The best runoff estimation uses the Bransby William method in units of l/hr with the root mean square of 7.95.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".