Integrated Hydraulic Modelling, Water Quality Modelling and Habitat Assessment for Sustainable Water Management: A Case Study of the Anyang-Cheon Stream, Korea
Bibliographic record
Abstract
Recent ecological stream restoration projects have focused on expanding the water-friendly space of streams, promoting the health of aquatic ecosystems, and restoring various habitats, which raise the need for relevant research. Applying integrated environmental analysis, this study quantifies the change in hydraulic characteristics before and after the restoration projects through physical habitat simulation and links the results of physical impacts to estimate benefits of increase in water quality and aquatic ecosystem health due to the implementation of the project. For this, the study area is a 3.3 km long reach of the Anyang-cheon Stream, Korea. Field monitoring revealed that five fish species are dominant and sub-dominant, and account for 76% of the total fish community. To assess the change of before and after ecological stream restoration project, the River2D and Coastal Modelling System (CMS)-Flow 2D models were used for hydraulic and water quality simulations, respectively. For the habitat simulation, the HSI (Habitat Suitability Index) model was used. In addition, the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) was used to calculate changes in water quality index and to examine changes in habitat areas with an integrated quantitative index, the methodology of Zingraff-Hamed et al. was adopted. It was found that the ecological stream restoration project significantly increased for the eco-friendly area. In addition, the changes in water quality and habitat suitability grades before the ecological river restoration project were improved to two stages and one stage, respectively. This study applied the integrated analytical framework as a policy/project assessment tool and the results of this study will be useful for the integrated water management policy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".