Scenario Analysis of Drinking Water Infrastructure Rightsizing in Flint, Michigan: Model Census Tract Results
Bibliographic record
Abstract
Sustainable urban systems require appropriately sized infrastructure. However, when cities face economic and population decline, hardened infrastructure - such as drinking water distribution systems – is difficult and costly to modify. This dataset provides the results of a modeling exercise performed using EPANET (Version 2.2) to examine potential scenarios for rightsizing water infrastructure in a shrinking city. The modeling was performed utilizing a full-scale, calibrated hydraulic model of the drinking water distribution system in Flint, Michigan obtained through a Memorandum of Understanding between the City of Flint and Wayne State University. For this analysis, in addition to a base model, we evaluate six scenarios involving either only decommissioning of pipes alone or both decommissioning and downscaling replacement of pipes. Model scenarios were run for three weeks (504 hours). The first two weeks (335 hours) were used to stabilize the system. System conditions (e.g., pressure in pounds per square inch (psi) and water age (hours)) at 15,936 locations in the system were recorded every hour, resulting in 168 measurements for each location in the system, a total of 2,677,248 measurements per hour. The minimum and maximum pressure, maximum water age (95th percentile), average pressure, and average water age (50th percentile) are computed during the third week of simulation. This dataset describes theoretical changes associated with each scenario as well as the pressure and water age resulting from EPANET modeling. Additionally, socio-economic data are also included. This dataset is paired with a second dataset available at https://doi.org/10.22237/waynestaterepo/data/1729036800/a. 894 record dataset Data Dictionary: GEOID: 14-digit Census Bureau geographic identifier based on 2010 Census; Scenario: Model Scenario (0-6); MinP: Minimum Water Pressure (psi); MaxP: Maximum Water Pressure (psi); AvgP: Average Water Pressure (psi); Ag50: Average Water Age (hrs); Ag95: Maximum Water Age (hrs); AgChn50: Change in Water Age (hrs) From Base Model (Scenario 0); AgChn95: Change in Water Age (hrs) From Base Model (Scenario 0); PercVacant: Percent Vacant Parcels in Census Tract; Distress: Distressed Communities Index (DCI) [Sadler RC, Gilliland JA, Arku, G. An application of the edge effect in measuring accessibility to multiple food retailer types in Southwestern Ontario, Canada. International journal of health geographics. 2011; 10, 1-15.]; PrcNW: Percent Non-White [U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates. Available at: www.data.census.gov. Accessed: 7 July 2024]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".