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Record W2308711047 · doi:10.14796/jwmm.r227-11

Optimal Design of Urban Drainage Systems using Genetic Algorithms

2007· article· en· W2308711047 on OpenAlexaffvenue
Paul F. Boulos, Trent Schade, Christopher W. Baxter, Misgana K. Muleta

Bibliographic record

VenueJournal of Water Management Modeling · 2007
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsCoquitlam College
Fundersnot available
KeywordsDrainageComputer scienceGenetic algorithmAlgorithmBiologyMachine learningEcology

Abstract

fetched live from OpenAlex

Control of sewer overflows is vital to reducing risks to public health and protecting the environment from water pollution.Sewer overflows are a leading cause of water pollution in the nation's lakes, streams and inland bays.The untreated sewage from these overflows contains microbial pathogens, suspended solids, toxics, nutrients, trash, and other pollutants that deplete dissolved oxygen and can contaminate our waters, causing serious water quality problems and threatening drinking water supplies, fish and shellfish.This sewage can also back up into basements, causing property damage and creating threats to public health for those who come in contact with the untreated sewage.There are about 19,500 sewer systems nationwide designed to handle an average daily flow of roughly 50 billion gallons of raw sewage (Nicklow et al., 2004(Nicklow et al., , 2006)).Sanitary sewer overflows (SSOs) or combined sewer overflows (CSOs) may release partially treated or untreated sewage to surface waters.High wet weather flows from rainfall-derived inflow and infiltration (RDII) can exceed system capacity, resulting in an SSO.The measured volume for this type of SSO is typically much greater than other causes of SSO.SSOs are most frequently caused by grease and debris blockage.Other causes for Optimal Design of Urban Drainage Systems using Genetic AlgorithmsSSOs include sediments buildup, pipe breaks, leaking manholes, offset joints, equipment failures, undersized sewer pipes, power outages, and other reasons.When an SSO occurs, sewage flows into streets, playgrounds and streams.CSOs occur in older combined sewer systems that were designed to carry both sanitary sewage and storm water runoff to a wastewater treatment plant (WWTP).Under dry conditions, the WWTP treats the sewage and then discharges it to a water body.During periods of heavy rainfall or snowmelt, however, the wet weather volume in the combined sewer system exceeds the available hydraulic capacity of the sewer system or treatment plant.This leads to the discharge of excess wastewater directly to nearby streams, rivers, or other water bodies.Combined sewer systems in the United Sates serve roughly 746 communities containing about 40 million people.Although there are combined sewers in 32 states and the District of Columbia, they are mostly located in the Northeast and Great Lakes regions, and the Pacific Northwest (U.S. EPA, 2004).With the growing expectations by the public for quality services, the U.S. Environmental Protection Agency (EPA) under the authority of the Clean Water Act adopted by Congress has implemented pollution control programs and set wastewater standards for the industry.In order to meet these requirements, comprehensive modeling and analysis of these sewer systems becomes necessary for developing sound cost-effective solutions for enhancing system integrity and performance to reliably convey sewer flows without surcharging, overflows, flooding, and backups.Today, many wastewater utilities and engineering consulting companies utilize drainage network simulation models to plan improvements and design better systems.Technology to achieve these improvements includes: the addition of new sewer pipes or treatment capacity as well as increasing conduit capacity (bigger interceptors), more storage volume, and pumping capacity.Current practice involves a tedious trial-and-error evaluation procedure that seldom leads to the most effective or most economical solutions for upgrading collection systems.This requires using the drainage network simulation model to evaluate the hydraulic performance of the existing system with different design alternatives (modifications) under a range of loading conditions.The design that meets the target hydraulic criteria for the lowest cost is then selected from among the alternative designs.The complexity of this manual trial-and-error procedure increases exponentially with the number of proposed system modifications and corresponding operating conditions.It is important to point out, however,

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.215
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2007
Admission routes2
Has abstractyes

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