A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis
Bibliographic record
Abstract
Abstract Regional flood frequency analysis forms the basis for ascertaining design thresholds for extreme flow events for the purpose of resource management and design of hydraulic structures, especially at ungauged or partially gauged basins. A crucial step in this analysis is transferring available information from gauged sites to ungauged sites, which is achieved through delineation of homogeneous regions encompassing multiple catchment locations, followed by the formulation of a flood estimation model. While this process has been accomplished through a range of statistical homogenization alternatives, the present study offers a new approach rooted in the theory of complex networks, offering considerable advantages over what is traditionally followed. Data from 202 sites in Australia representing catchments of varying geographic, climatic, and vegetation attributes are used to assess the alternative proposed. The results are examined via (1) direct comparison of the location and number of homogeneous neighbors from network theory with results using canonical correlation analysis (CCA) and (2) assessing the accuracy of estimated flood quantiles by applying a common model that estimates flood quantiles using information from the two alternate groups of homogeneous sites (from network theory and CCA). Results show that network theory offers merit in delineating homogenous regions, with resulting design flood estimates showing improvements across different return periods compared to the CCA alternative used.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".