Urban flood modelling: Challenges and opportunities - A stakeholder-informed analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modelling urban floods is essential for disaster prevention, yet it faces limitations in accuracy due to technical, operational, and functional constraints. The study employs a primary market research analysis to explore the perspectives of both academic and non-academic experts in urban flood modelling (UFM). Identified issues include inadequate spatial and temporal model resolution, high data requirements, and non-intuitive user interfaces. Opportunities are recognized in integrating flood risks, social dynamics, future land use, climate data, and real-time information while reducing computational costs and improving usability. To address these aspects, a holistic framework has been proposed that includes features like hybrid-physics AI modelling, real-time data integration, compound flood simulation, transfer learning, sociohydrology tools, future scenario forecasting, cloud-based pipelines, interoperability, compatibility, and AI-enhanced parallel computing and user interface. Finally, we presented an ecosystem map illustrating stakeholder roles in UFM. The findings offer valuable insights into refining UFM for enhanced urban flood resilience.
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.
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 | 0.000 | 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 | 0.001 |
| 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 it