A GIS-Based Fuzzy Hierarchical Modeling for Flood Susceptibility Mapping: A Case Study in Ontario, Eastern Canada
Bibliographic record
Abstract
Natural disasters such as floods have severely destroyed the natural environment and infrastructure because of their destructive effects and caused socio-economic losses. In the present study, the authors attempt to present a flood hazard susceptibility map of an eastern region in Ontario, Canada to facilitate flood prevention and mitigation. To this purpose, a combination of Multi-Criteria Decision-Making (MCDM) model and Geographic Information System (GIS) has been considered. Herein, an Analytical Hierarchy Process (AHP) model is applied based on Triangular Fuzzy Numbers (TFNs) in a GIS environment. A total of eight quantitative criteria including elevation, land use/land cover, geology, rainfall, drainage density, slope, soil-type, and distance from river have been used for the flood modeling. Fuzzified pairwise comparison matrices of values have determined the Importance Weights (IWs) of these criteria in Saaty’s scale. By calculating IWs, the impact of each effective criterion on flood risk was investigated using the fuzzy AHP method. The consistency Index of each pairwise comparison of criteria has been checked. Based on the calculated IWs result of each criterion, the precipitation, slope, and soil criteria play significant roles as the most eminent flood occurrence criteria. In addition, the obtained results demonstrate percentages of flooded areas and the flood hazard index of the study area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".