A GIS-supported fuzzy-set approach for flood risk assessment
Bibliographic record
Abstract
This study presents a geographic information system (GIS)-supported three-dimensional fuzzy risk assessment approach (3D GIS-FRA) for flood risk assessment that is based on the development of a fuzzy-set risk model, 3D GIS mapping, and a hydro-statistical simulation. Using GIS and a digital elevation model (DEM), urban settings under different levels of flood risk are visualized and hydraulic simulations conducted for various river flow scenarios to determine the flow rates for specified risk levels. Then, a statistical analysis is carried out using historical records to establish a set of risk criteria that consider critical factors that affect the peak flow rate. Finally, the developed fuzzy-set risk model is applied to examine the flood risks using the outputs from the hydraulic models and statistical analysis. The developed method is applied to a section of the Red River in Southern Manitoba, Canada. The 3D GIS-FRA results indicate that there is a possibility of having a highly risky situation for the upper-bound extreme condition for the study area, although only limited impacts are expected for a 25-year flood. For the 75-year flood scenario, the overall flood risk level is high for the whole area. The results indicate that the developed risk analysis system is useful for systematically quantifying the flood risks and the related system uncertainties.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".