Urban Flood Mitigation by Implementing LIDs (Case Study: Bendung Watershed in Palembang City)
Bibliographic record
Abstract
Urban areas continue to be affected by flooding, necessitating more sustainable and effective adaptation strategies and mitigation initiatives. This study investigates the potential flood reduction capability achieved through implementing various green infrastructures known as low-impact development (LID). The Bendung watershed, in the center of Palembang City, with a total area of 18.37 km2, is used as the study area to evaluate the performance of LID infrastructure in reducing flood parameters, including total runoff volume, peak runoff discharge, runoff coefficient, and flooding area. Five types of LID infrastructure were simulated, namely bio-retention cells, rain gardens, permeable pavements, rain barrels, and recharge wells. The flood simulations were performed using four design storms with 2-, 5-, 10-, and 25-year return periods. Hydrologic and hydraulic modeling and simulations were carried out using PCSWMM Professional 2D, and the results were integrated with ArcMap to map the flood inundation. The results of this study demonstrate that with only 9.81 percent of the area occupied by LIDs, a flood reduction of more than 30% can be achieved. In addition, implementing LIDs can help restore the watershed’s hydrological condition to its natural state, as indicated by the decrease in the runoff coefficient. Thus, implementing LIDs in a sustainable urban drainage system must be widely promoted in many urban areas, especially in developed countries like Indonesia. This study can be used as a reference for the local government and authorities to create policies and regulations to establish sustainable flood mitigation measures in Palembang City.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".