Low Impact Development Modeling to Assess Localized Flood Reduction in Thailand
Bibliographic record
Abstract
The causes and impacts of, and emergency responses to, the recent catastrophic flooding in northern and central Thailand, including Bangkok, are reviewed.A number of short term and long term engineering solutions have been proposed to avoid or minimize future flooding impacts.Low impact development (LID) technologies might be one reasonable, sustainable, approach to solving urban drainage and water quality problems in Bangkok, but the local urban approaches should be integrated with watershed wide planning efforts.PCSWMM was used to model single and multiple LID technologies in a case study of a peri-urban village near Bangkok as a preliminary exploration of LID benefits in terms of stormwater quantity and quality.A 2 y design storm for Thailand was used in the modeling.The design plans of LID technologies, using either CAD or Google SketchUp, were visualized through Google Earth, and were costed using local information.All LID scenarios reduced combined sewer overflow (CSO) volume, CSO pollutant loadings and the durations of surface flooding, although single rain barrels installed at all houses had a relatively small (4% to 9% reduction) impact.This may be related in part to the fact that a 2 y storm in Thailand is more similar to a 50 y or 100 y storm in northeastern North America.The combined rain barrel and bioretention cell scenarios offered the greatest control in reducing CSO discharges, but the costs may be prohibitive in Thailand at present.
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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 | 0.000 |
| 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 | 0.001 | 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".