Applying neural networks combined with Monte Carlo simulation in dam operations to obtain operational, economic and environmental gains
Bibliographic record
Abstract
The waste of potable water is a problem that affects a population’s supply and the environment, raising the need for studies focusing on the adoption of efficient actions and modern technological resources, such as artificial intelligence (AI), for sustainable water management. However, in the literature there are few studies on the operational, economic and environmental benefits of using AI in dam management. In addition, no study has been found on this topic addressing the Cantareira system, located in the metropolitan region of São Paulo, Brazil, which is one of the largest water supply systems in the world. This work presents an approach combining an artificial neural network and the Monte Carlo simulation method for floodgate control in the Cantareira system. Furthermore, parameters are explored that make the simulations of water collection and distribution more realistic. The results (root mean squared error (RMSE) = 0.076 and R2 = 0.963) confirm the viability of using the proposed approach to minimize water waste and flood risks, as well as to increase efficiency in water resource management. Furthermore, this study advances the state of the art by presenting a set of operational, economic and environmental benefits directly associated with the adoption of AI in floodgate management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".