An Investigation of the Current Situation of Floodplain Mapping in British Columbia: A Fuzzy Rule-Based Approach
Bibliographic record
Abstract
The increased intensity of floods has become an emerging phenomenon in British Columbia, Canada. Flooding causes significant damage to properties and the built environment. The flood damage is magnified by the unthoughtful location choices for settlements and incoherent land use policies. This has severely restricted the capacity to build resilience to adapt to the unpredictable challenges of floods. Therefore, a thorough understanding of the current state of the floodplain maps that entail the spatial distribution of floods and the associated risks to the communities is paramount. Therefore, the British Columbia Real Estate Association (BCREA) attempted to identify pathways to increase awareness of the current state of the floodplain maps and prepare an updated inventory and its vitality to build resilient communities. The survey results suggested that 38.5% of the communities have created or updated their floodplain maps since 2015, and 62% of the maps meet the British Columbia Flood Hazard Area Land Use Management Guidelines. However, a survey conducted in 2020 suggested an increase in the response rate by 12.8%, which indicates a growing urgency to mitigate flood risks. Due to a lack of expertise and pertinent knowledge, 46% of the communities could not create or update the floodplain maps. In addition, the lack of provincial funding was identified as a key impediment to the floodplain mapping that was experienced by 37.5% of the communities. A schematic perception–action–state–accessibility–usage (PASAU) framework was proposed in this study to confirm the current state of floodplain mapping. The British Columbia, Canada, regions were ranked following a fuzzy rule-based approach to assess the nature and status of preparations for floodplain maps. The result suggests that the Northern Territories, Canada, lie at the low and others are at the medium scale. Communities that scored low were attributed to a lack of funding, in-house expertise, data, and planning endeavors. This study suggested actions for different tiers of the government to make the communities safer and more resilient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".