Are all data useful? Inferring causality to predict flows across sewer and drainage systems using directed information and boosted regression trees.

Hu, Yao; Scavia, Donald; Kerkez, Branko

Hu, Yao; Scavia, Donald; Kerkez, Branko.

Afiliación

Hu Y; Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, United States. Electronic address: huya@umich.edu.
Scavia D; School for Environment and Sustainability, University of Michigan, Ann Arbor, United States. Electronic address: scavia@umich.edu.
Kerkez B; Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, United States. Electronic address: bkerkez@umich.edu.

Water Res ; 145: 697-706, 2018 11 15.

Article en En | MEDLINE | ID: mdl-30216864

Asunto(s)

Modelos Teóricos; Lluvia; Ciudades; Michigan

Palabras clave

Boosted regression trees; Causality; Data-driven model; Directed information; Flow prediction

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Lluvia / Modelos Teóricos Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies País/Región como asunto: America do norte Idioma: En Revista: Water Res Año: 2018 Tipo del documento: Article

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