A Systematic Review of Participatory Approaches in Flood Risk Management: Methods and Applications
Bibliographic record
Abstract
Flood risk management (FRM) increasingly integrates participatory approaches to enhance resilience and effectiveness by engaging local communities and stakeholders. This systematic review synthesizes findings from 22 peer-reviewed articles published between 2015 and 2024, highlighting the tools, stakeholders, levels of participation, outcomes, and challenges associated with participatory FRM. The review identifies key engagement tools such as participatory mapping, community workshops, and digital platforms, noting their varied effectiveness in different contexts. Stakeholder involvement spans residents, government agencies, and NGOs, with diverse contributions enhancing the contextual relevance and acceptance of FRM strategies. Levels of participation range from consultative to collaborative and fully empowering, with higher engagement linked to more resilient and adaptive outcomes, albeit requiring more resources and time. Outcomes demonstrate that participatory approaches improve community preparedness, enhance flood management plans, and integrate local knowledge effectively. However, challenges persist, including resource constraints, stakeholder conflicts, and communication barriers, necessitating adaptive management and innovative engagement strategies. The findings underscore the need for policymakers and practitioners to prioritize participatory methods to develop inclusive and robust FRM frameworks. Future research should focus on scalable participatory models, the integration of advanced technologies, and the evaluation of long-term impacts on community resilience, providing a roadmap for the enhanced application of participatory approaches in diverse contexts.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".