Floods and Countermeasures Impact Assessment for the Metro Colombo Canal System, Sri Lanka
Bibliographic record
Abstract
A 15th-century canal system in the Metro Colombo area of Sri Lanka was studied to identify its capacity in controlling floods. The canal system was modelled by MIKE FLOOD for 10, 25 and 50-year return periods of rainfalls to achieve respective floods. The impacts of the considered rainfalls were analyzed considering the flood levels, inundation distributions and affected people. Two simulation scenarios which were based on the river boundary conditions were carried out in the study and they were categorized as favourable and least favorable. It was identified that under the existing conditions, the canal system could handle only a 10-year rainfall flood event under the favourable condition. Therefore, the canal system's sustainability for future anticipated extreme events is suspicious. To mitigate such floods, four countermeasures were introduced and their impacts were analyzed. When the countermeasures were introduced one at a time, the flood water levels were lowered locally and they were not up to the flood safety levels of the surrounding area. When all four countermeasures were introduced together, the flood water levels were significantly lowered below the flood safety levels for a 50-year design rainfall under the favourable condition. The reduction of the inundated area was significant in the case of applying all four countermeasures together. In that case, a 46% inundation area reduction and a 49% reduction in the number of affected people were achieved.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".