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Record W7132990155

Linear System Identification of an Urban Drainage System with the Integrator-Delay-Zero Model

2024· dissertation· W7132990155 on OpenAlexfundno aff
Chuanning Wei

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaConnaught FundUniversity of Toronto
KeywordsElectrical conduitFlooding (psychology)Identification (biology)DrainageDrainage system (geomorphology)Drainage networkSystem identificationController (irrigation)Stormwater
DOInot available

Abstract

fetched live from OpenAlex

Urban drainage systems (UDS) are necessary for protecting cities and their residents from flooding eventsas well as protecting the environment from urban waste. However, safe control is difficult to achieve because these networks of water conveyance infrastructure are high-dimensional (i.e., they consist of many conduits and nodes) and the dynamics of water flow are nonlinear. The availability of a simple yet sufficiently representative dynamical system model can facilitate automatic controller design. Therefore, this thesis provides a principled approach based on the integrator-delay-zero model to reduce a complex UDS consisting of many cascading and merging conduits to a time-delayed linear system. Simulations using the Storm Water Management Model (SWMM5) engine confirm the validity of the proposed modelling approach.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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