Combined Urban and Rural Models for Integrated River Basin Management
Bibliographic record
Abstract
Traditionally, urban and rural hydrology consider rainfall runoff processes very differently.Usually they are modeled independently, and linked, if at all, by data transfer interfaces.Only a few meso-scale models are known that are capable of modeling both urban and rural catchments simultaneously at a high spatial resolution; and, even in these, detailed interactions between the different hydrologic sub-systems are hardly taken into account.In the field of urban hydrology, successful attempts have been made to model sewer systems including wastewater treatment plants (WWTP) and receiving water bodies.However, pollutants from rural areas are mostly neglected when modeling the water quality of receiving water bodies.The complexity of an integrated model, taking into account rural and urban systems, as well as their interactions, is one of the major reasons for the lack of such a model.However, such models are urgently needed for the realisation of the EU-Water Framework Directives (EU-WFD, 2000) which aims at achieving ecological improvements.In this chapter, an approach is described, which takes into account the interaction of urban and rural hydrology by a 2-layer modeling system.For this approach, two existing models have been modified and combined with pre-defined points of interaction.The main goal of the development was improved flow peak simulation in streams loaded with multiple combined sewer overflows from several sewer networks.Criteria for water quality in
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".