HYDROLOGICAL MODELING USING SWAT IN THE DECISION-MAKING PROCESS FOR THE CONSERVATION OF RIVER BASINS.
Bibliographic record
Abstract
The conservation of watersheds is a concern in all countries because of the scarcity of water for capitation and use and the anthropogenic degradation of this natural resource. Thus the objective of this study is to make a systematic review on the themes ecosystem service, hydrological modeling using the SWAT model. Emphasizing the importance of this theme in the decision-making process in the management of water resources. The research is exploratory, having as main method the literature/scientific review. The bibliographic survey used was carried out on the Scopus platform, in chronological order in the range from 2018 to 2022. The two years of the health crisis were the years of greatest scientific production. Two publications stood out for the number of citations: A review of SWAT Applications, performance and Future needs for Simulation of Hydro-Climatic Extremes and, Comparison of the SWAT and Invest models to determine hydrological Ecosystem service Spatial Patterns, Priorities and trade-offs in a Complex Basin, both published in 2020. The most prominent countries in research publications in the area of Environmental Science were China, USA, Canada, Germany, Brazil and Norway. The relevance of studies involving this theme become evident because they are tools used in the decision-making process in water management, showing up as a vast field for research in Latin America and South America, but specifically in Brazil for its continental dimension and its diversity.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".