Pemodelan Degradasi dan Agradasi Dasar Sungai dengan Beberapa Persamaan di Sungai Winongo Yogyakarta
Bibliographic record
Abstract
Calculating riverbed degradation and aggradation is essential in designing riverbank protection structures, particularly for determining foundation depth. Excessive degradation can compromise foundation stability, significantly increasing the risk of structural failure. Numerous predictive models for egradation and aggradation have been developed by researchers, highlighting the importance of selecting an equation that closely aligns with the specific characteristics of the river to achieve optimal design accuracy. This study aims to determine the most suitable predictive model for riverbed degradation and aggradation. A case study was conducted along a 43.75 km of the Winongo River Yogyakarta. The simulation involved riverbed sediment data collected at 500 m intervals from upstream to downstream, and secondary discharge data comprising average daily discharge for both wet and dry months. The selected grainsize parameter follows standards in HEC-RAS 6.3.1, with the Meyer Peter Müller equation applied to d90, Engelund Hansen to d50, and Laursen Copeland to d84. Simulation results of riverbed degradation were then compared against observed conditions of riverbank erosion. Riverbank steepness or protective structure failure indicates excessive riverbed degradation, while stable conditions suggest otherwise. Based on the simulations conducted on the Winongo River, the Engelund Hansen equation provided average degradation estimates more consistent with field conditions than the other two equations.Keywords: degradation and agradation, transport sediment equations, HEC-RAS, river bank
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".