Optimization of Water Resources to Counteract the Effects of Water Deficit Using the WEAP Model
Bibliographic record
Abstract
The water deficit generates a great impact in regions where demand for this resource is of vital importance for socioeconomic development.The high concentration of urban centres, the poor management of the resource, the various climatic changes, are some of the factors that cause its limitation.This research aims to analyze the best alternative to optimize the resource and meet the demands in areas where its availability and supply are scarce.A hydrological simulation model of the Coata River Basin (CRB) was carried out using the Water Assessment and Planning System (WEAP).Data such as precipitation, temperature, relative humidity, wind speed, and flow were extracted from the historical records of 10 rainfall stations for a period of 50 years .Three scenarios were established in the CRB, the construction of a dam (S1), the implementation of a diversion (S2) and the implementation of a sprinkler irrigation system ( S3).The results revealed that S1 has a supply of 1103.1 MMC and a demand of 1308.4MMC, leaving an unsatisfied demand of 205.3 MMC.The S2 has a supply of 1103.1 MMC and a demand of 1285.6 MMC, leaving an unsatisfied demand of 182.5 MMC.Finally, the S3 presents a supply of 1103.1 MMC and a demand of 912.3 MMC, with the demand for water being fully covered.The implementation of a sprinkler irrigation system proved effective in improving the volume of water required in the CRB.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".