Modeling low impact development potential with hydrological response units
Bibliographic record
Abstract
Evaluations of benefits of implementing low impact development (LID) stormwater management techniques can extend up to a watershed scale. This presents a challenge for representing them in watershed models, since they are typically orders of magnitude smaller in size. This paper presents an approach that is focused on trying to evaluate the benefits of implementing LIDs on a lot level. The methodology uses the concept of urban hydrological response Unit and results in developing and applying performance curves that are a function of lot properties to estimate the potential benefit of large-scale LID implementation. Lot properties are determined using a municipal geographic information system database and processed to determine groups of lots with similar properties. A representative lot from each group is modeled over a typical rainfall year using USEPA Stormwater Management Model to develop performance functions that relate the lot properties and the change in annual runoff volume and corresponding phosphorus loading with different LIDs implemented. The results of applying performance functions on all urban areas provide the potential locations, benefit and cost of implementation of all LID techniques, guiding future decisions for LID implementation by watershed area municipalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".