Integration of heuristic knowledge with analytical tools for the selection of flood damage reduction measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heuristic knowledge has been integrated with analytical tools to support decision making for flood management. Development of an expert system called Intelligent Flood Management System for the selection of appropriate flood damage reduction measures for a given area is described. The selection of flood damage reduction measures is based on hydraulic, hydrological, geotechnical, environmental, and economic factors related to the river system and the area to be protected from floods. The knowledge base of the Intelligent Flood Management System is generic and can be used to identify a suitable flood management option for any area. The model base of the Intelligent Flood Management System consists of the hydraulic analysis package HEC-RAS, the flood damage analysis program HEC-FDA, and a model for economic analysis. The graphical user interface is developed for effective communication with the system. The developed system has been implemented to identify appropriate flood damage reduction options for the town of Ste. Agathe in Manitoba, Canada using data from 1997 flood in the Red River Basin.Key words: flood control, flood management, structural measures, heuristic knowledge, decision support systems, expert systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it