Dynamic modeling of flood management policies
Bibliographic record
Abstract
Economic and social impacts related to flood disaster are two important and interdependent issues addressed by flood management policies. Economic impacts include structural and non-structural damages caused by the floods and the social impacts of flood disaster are mainly related to evacuation, where public response to disaster warning plays an important role. This paper presents a system dynamics model that captures dynamic interaction between different components of the flood management system. The model provides a platform for evaluation of the consequences of various policy alternatives for flood management. The operation of reservoir and floodway has been simulated. Operating rules are developed for high flow/flood years to minimize flooding. Alternative operating rules are explored by changing reservoir storage allocation and outflows. Impacts on the flood management capacity of the reservoir are investigated by simulating gated spillway in addition to an existing unregulated spillway. Flood damages to buildings and infrastructure are calculated. Sensitivity analysis is performed on the reservoir levels at the start of the flood season and outflow from the reservoir. The modeling work on economic impacts of flood management policies is complete. However, the work on social aspects of flood management especially public response to flood warning and people’s perception of risk with special relevance to evacuation planning is in progress. The model is implemented for the Red River basin in Canada using recorded data of large flood events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".