Advancing Ice-jam Flood Risk: Integrating Dynamic Adaptive Behavior into Agent-based Model of Fort McMurray
Bibliographic record
Abstract
Human behaviors have changed as ice-jam flooding has become more prevalent, impacting both flood hazard and vulnerability as a function of flood risk. These dynamic adaptations can be developed by both governments (e.g., artificial breakup and dike installation) and individuals (e.g., flood-proofing and elevating houses). The interaction between these top-down and bottom-up measures provides a complex socio-hydrological system. However, the traditional assessment of ice-jam flood risk lacks an appropriate consideration of evolving human behaviors and their interactions with static assumptions on human adaptations. We build an agent-based model to assess the ice-jam flood risk with top-down and bottom-up adaptive strategies (artificial breakup and flood-proofing). The individuals’ behaviors are influenced by the possible reduction in flood risk at the individual level by artificial breakage over time. Also, the government’s behavior is influenced by the possible reduction in total flood risk by the dynamic adaptive behavior of individuals (flood-proofing). Thus, micro levels’ behavior can dynamically lead to macro phenomena, and macro phenomena define micro levels’ behavior over time. This model is applied to Fort McMurray along the Athabasca River, Canada, with a long history of ice-jam flooding. Also, we perform a variance-based global sensitivity analysis to investigate the individual effect of model factors and their joint effects on ice-jam flood risk. The results show that although the artificial breakage by the government leads to a regime shift and a considerable decrease in the ice-jam flood risk, it decreases the number of the newly adapted residents to flood-proofing and the role of residents in ice-jam flood risk. This study can provide a good understanding of the important role of dynamic adaptive behavior in ice-jam flood risk and pave the way for better Building flood resilience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".