A comparative evaluation of flood mitigation alternatives using <scp>GIS</scp>‐based river hydraulics modelling and multicriteria decision analysis
Bibliographic record
Abstract
Abstract A multicriteria framework is developed for the selection of optimal flood mitigation and river training measures in a selected reach of Zaremroud River in Northern Iran. A river model, Hydrologic Engineering Center River Analysis System, combined with geographic information system analysis is used to simulate water levels for steady, gradually varied flow and mapping inundated flood extents. The modelling is performed for four different alternatives, considering various channel modifications with different dimensions and levee construction. Flood inundation area, flood level, flow velocity and stream power on the downstream and outside of the river bend are used as decision criteria for each alternative. Economic analysis is conducted to evaluate the cost‐effectiveness of each alternative. The decision analysis method, technique for order of preference by similarity to ideal solution, is used to compare different flood hazard mitigation measures based on risk, and environmental and economic impacts criteria. The findings of the analysis are that a levee construction at the right side of the river bank adjacent to the residential area is superior to the other three alternatives, which is confirmed using a scenario analysis of different flood mitigation 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".