Inverse Flood Risk Modelling of The Upper Thames River Basin
Bibliographic record
Abstract
This report aims to present an alternate approach to climate change impact mod- elling of water resources. The focus of the project is on the analysis of existing wa- ter resources management guidelines specifically targeting critical hydrologic events (extreme floods in this case). The critical hydrologic events are converted to their corresponding meteorologic conditions via use of an event based hydrologic model. The local climatic signal is generated by use of a non-parametric weather generator linked to outputs from a global climate model for three climate scenarios, and their corresponding frequency curves generated. Then, a critical hydrologic event of inter- est is selected, its corresponding meteorological condition obtained, and its frequency of occurrence (one for each climate scenario) determined.\nA scenario selected specifically to study the problem of flooding in the basin showed more frequent occurrence of flooding for nearly all magnitudes of floods. An- other scenario, selected for studying droughts depicts a lesser tendency of extreme flooding events. Therefore, ranges of estimates of changes of frequency of occurrence of critical hydrologic events are obtained in response to changing climatic conditions. Based on these estimates, recommendations for changing current basin management guidelines are provided. They are categorized into three distinct categories: (i) regula- tory (where a review of rules, regulations and operation of current flood management infrastructure are suggested); (ii) budgetary (where investment in new infrastructure, as well as increased maintenance costs of present and future infrastructure, can lead to a need of having higher operating budgets); and (iii) engineering (recommending a review of current design standards of critical infrastructure).
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".