Reconstruction of Hydraulic Management of a Water Distribution System Using Genetic Algorithms
Bibliographic record
Abstract
An epidemiologic study of childhood leukemia and central nervous system cancers that occurred in the period 1979 through 1996 in Dover Township, N.J., is being conducted. The study is exploring a wide variety of possible risk factors; one being the exposure to groundwater contaminants that occurred through private and community water supplies (i.e., the water-distribution system serving the area). For this purpose a model of the water-distribution system has been developed and calibrated through an extensive field investigation. The components of this water-distribution system, such as number of pipes, number of tanks, and number of supply wells in the network, have changed significantly over a 35-year period (1962 through 1996) - the time frame established for the epidemiologic investigation. Manual reconstruction of the historical management of the system is time consuming, labor intensive, and costly, given the complexity of the system and the time constraints imposed on the study. In an effort to reduce the required computational time, the problem was formulated as an optimization problem. For each month of the study period, the management strategy was arrived at by obtaining a solution to the optimization problem. In this study, it is assumed that the water-distribution system was operated in an optimum manner at all times to satisfy the minimum and maximum pressure constraints and tank level constraints in the system. Given these assumptions, we have used Genetic Algorithms along with the EPANET water-distribution network solver, to solve the optimization problem and develop the historical management strategy used by the water-distribution system serving the Dover Township area, NJ. This process assisted in reducing the required solution time and generated a historically consistent management strategy for the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".