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Record W3206575243 · doi:10.1061/jswbay.0000968

Analytical Derivation of Urban Runoff-Volume Frequency Models

2021· article· en· W3206575243 on OpenAlexaff
Sonia Hassini, Yiping Guo

Bibliographic record

VenueJournal of Sustainable Water in the Built Environment · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSurface runoffSaturation (graph theory)Environmental scienceHydrology (agriculture)Return periodInfiltration (HVAC)StormwaterRunoff modelProbabilistic logicRunoff curve numberDrainage basinStormwater managementSoil scienceMathematicsStatisticsGeographyGeotechnical engineeringGeologyFlood mythMeteorologyEcology

Abstract

fetched live from OpenAlex

The analytical probabilistic approach has been investigated and occasionally applied in urban stormwater management for more than three decades. Many expansions and improvements have been made since its first appearance. However, there are still areas for the model to expand and improve. This study illustrates the derivation of the exact frequency distributions of the runoff-event volume considering both infiltration and saturation excess runoff generation processes. This new model is more accurate than the previously developed ones and can be used for the planning and design of certain stormwater management practices featuring water quality control, such as low-impact development practices. The model can effectively estimate the runoff volume of a small urban catchment with different return periods. It is applied to an actual small urban catchment under different soil saturation levels. The model was able to detect the minimal changes in the values of soil saturation; for a fixed return period, the model produced different runoff volumes under very close levels of soil saturation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.210
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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