MétaCan
Menu
Retour à la cohorte
Enregistrement W2332523707 · doi:10.14796/jwmm.r235-01

The Use of Decision Analysis and Watershed Modeling to Investigate E.coli Potential Sources and Solutions in Lake Tuscaloosa Watershed, Alabama

2009· article· en· W2332523707 sur OpenAlexvenueno aff
Laith Alfaqih, Robert E. Pitt

Notice bibliographique

RevueJournal of Water Management Modeling · 2009
Typearticle
Langueen
DomaineEngineering
ThématiqueWater resources management and optimization
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Alabama
Mots-clésWatershedEnvironmental scienceHydrology (agriculture)Water resource managementGeologyComputer scienceGeotechnical engineering

Résumé

récupéré en direct d'OpenAlex

Lake Tuscaloosa, an artificial impoundment that serves as a public water supply, is located in Tuscaloosa and Fayette counties in the State of Alabama, in the Southeastern United States.Recent studies and monitoring of the lake show high levels of E.coli bacteria in the upper parts of the lake (near the main stream entrances) during periods of high stream flow.These high levels of E.coli are a concern for many different interested parties in the area.The city is under pressure to strengthen its management, monitoring, and control of existing and future pollutant sources (mostly land development) around the lake that is in its jurisdiction.Additionally, the city has to consider other sources of bacteria in the watershed outside of its jurisdiction as potential causes of these elevated bacteria levels.The decision analysis framework and modeling schemes developed as part of this research examine flow, E.coli sources and transport issues, along with potential solutions.The decision analysis framework assisted at different stages of the project during the collection and management of the information that helped in the analysis of the problems and solutions.The flow and E.coli watershed models assisted in the analysis of the available data for the watershed to identify locations, seasons, and flow ranges associated with the E.coli discharges.Developing a strategy to maintain the E.coli levels below the permissible limits in the watershed was challenging because many factors and information were needed for consideration during the data analysis and decision making parts of the research. IntroductionThe Lake Tuscaloosa watershed is located in Tuscaloosa and Fayette counties in the State of Alabama, in the Southeastern United States.The Lake, which was constructed on North River in 1970, serves as the major public water supply for the surrounding communities and is an important recreational water body in an area lacking in natural lakes.The watershed covers an area of approximately 1100 km 2 .The lake covers an area of approximately 24 km 2 and it holds about 150 million m 3 of water.The watershed and the lake are presented in Figure 1.1.This area normally has high rainfall (long term average of about 1400 mm/y), but is currently undergoing a severe drought, with about half of the normal rainfall having fallen during the last rain year.Even with this shortage, Lake Tuscaloosa has proven to be a reliable and sustainable water supply for the area.The reliability of this water supply has been an important component for the economy in the area.Recent studies and monitoring of the lake have shown high levels of E.coli bacteria, especially in the northern parts of the lake (near the main river entrances) during periods of high stream flow (O'Neil, 2005).These high levels of E.coli have been identified as a concern for many stakeholders in the area.E.coli is a type of fecal coliform bacteria that is usually found in the intestines of warm blooded animals such as humans, cattle, birds, and different wild animals and is commonly used as an indicator of domestic sewage contamination and the presence of possible pathogens.E.coli are mostly harmless bacteria commonly found in fecal discharges from warm blooded animals.Most strains are harmless, but some, most notably O157:H7, can cause serious illness in humans.In 1986, the EPA developed criteria for E.coli and Enterococci using currently accepted illness rates.These bacteria are assumed to be more specifically related to poorly treated human sewage than fecal coliforms.Many states and agencies therefore monitor E.coli as part of their surveillance monitoring activities.The typical analytical methods used are non-specific to the many E.coli stains; therefore, if E.coli are detected, it should not be assumed

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,271
Score d'incertitude au seuil0,539

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,200
Écart entre enseignants0,178 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2009
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Water Management ModelingMême sujetWater resources management and optimizationTravaux en français237 207