Automating Model Builds for Sequence Optimization of Flood Mitigation Investment Phases
Bibliographic record
Abstract
A process and a set of tools are proposed to automate the creation of an exhaustive set of flood mitigation scenarios modeled by the United States Environmental Protection Agency's Stormwater Management Model (SWMM), as well as to create a strategic planning framework that takes advantage of the exhaustive model scope.Using the proposed method, models are generated for each logical combination of independent flood mitigation alternatives at each possible phase of implementation (i.e. at every practical phase of investment).Cost estimates and flood risk reduction values are calculated for each flood mitigation alternative and organized into all possible sequences of implementation.The exhaustive set of progressive implementation scenarios is then used as a decision-making tool to provide flexibility in long term planning.Because each phase of investment can be implemented and function independently, large capital improvement programs can be constructed progressively without having to commit (politically and financially) to the cost of full implementation of the program.With the proposed process and the data produced, it is shown that strategic planning can be adaptive and may be adjusted to favour short term cost effectiveness, or optimized within a long term budget while being able to end investment at any phase in the program.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".