Coastal urban flood risk management: Challenges and opportunities − A systematic review
Bibliographic record
Abstract
Generational mechanisms and spatio-temporal evolution patterns of coastal urban flood risk involve complex interactions between climate change, sea level rise and human-induced factors, necessitating integrated adaptive flood management strategies to mitigate evolving vulnerabilities. This systematic review offers a thorough assessment of the challenges and strategic opportunities for sustainable adaptation in managing flood risk in coastal urban areas. It integrates emerging innovative technologies and financial solutions to identify promising approaches to implement mitigation strategies and improve coastal urban flood resilience. Enhancing governance and policy frameworks is crucial for the successful implementation of coastal urban flood risk management (CUFRM) plans. An innovative participatory planning framework is developed to promote flood management practices which are socially inclusive and equitable. Funding for green infrastructure and nature-based solutions and the strategic use of public-private partnerships are effective methods for advancing sustainable flood risk management (FRM). The advancements in emerging technologies, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), social media and digital twin technologies, provide dynamic and collaborative platforms for simulating flood scenarios and have potential to significantly improve CUFRM practices. In the end, a cross-country comparison of current practices in Australia, China, the Netherlands, the UK and the USA reveals a diverse range of approaches and valuable insights derived from regional experiences. The review provides a comprehensive analysis for researchers, policymakers and practitioners aiming to improve flood resilience in coastal metropolitan regions by learning from effective UFRM approaches that enhance governance structures, infrastructure resilience and funding mechanisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".