Dam-Break Risk Analysis and Mitigation at Pidekso Dam, Wonogiri Regency, Central Java, Indonesia
Bibliographic record
Abstract
This study analyzed the flood risk associated with potential dam break events at the Pidekso dam in Wonogiri Regency, Central Java Province, Indonesia. Embankment dams, such as the Pidekso dam, are susceptible to piping and overtopping, which can result in dam failure and severe natural disasters, causing loss of life and infrastructure damage. The study utilized HEC-HMS and HEC-RAS software to simulate dam break scenarios, generating maps of dam break flood discharge, flood inundation, and flood arrival time. The analysis revealed that overtopping scenarios resulted in a higher outflow discharge compared to piping scenarios, with a peak discharge of 14,821 m3/s. Flood inundation and arrival time maps were used to assess the risks to nearby villages. Moreover, based on the risk index calculation using the formula provided by the National Disaster Management Agency, the studied villages were classified into distinct risk levels. Specifically, one very low-risk, four low-risk, six medium-risk, and seven high-risk villages, with none classified as very high-risk. This study also recommends a combination of structural and non-structural solutions to mitigate the risks of a dam break. By implementing structural mitigation measures such as an emergency spillway and compound channel along the downstream river, the study achieved an 8.4% reduction in flood extent. While most villages showed no significant changes in their risk indices, Sinorboyo village, which was previously susceptible to flooding, benefited from enhanced protection measures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".