Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining.

Piadeh, Farzad; Behzadian, Kourosh; Chen, Albert S; Kapelan, Zoran; Rizzuto, Joseph P; Campos, Luiza C

Piadeh, Farzad; Behzadian, Kourosh; Chen, Albert S; Kapelan, Zoran; Rizzuto, Joseph P; Campos, Luiza C.

Afiliación

Piadeh F; School of Computing and Engineering, University of West London, St Mary's Rd, London W5 5RF, UK; School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.
Behzadian K; School of Computing and Engineering, University of West London, St Mary's Rd, London W5 5RF, UK; Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, Gower St, London WC1E 6BT, UK. Electronic address: kourosh.behzadia
Chen AS; Centre for Water Systems, Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4QF, UK.
Kapelan Z; Department of Water Management, Faculty of Civil Engineering and Geoscience, Delft University of Technology (TU Delf), Delft, Netherlands.
Rizzuto JP; School of Computing and Engineering, University of West London, St Mary's Rd, London W5 5RF, UK.
Campos LC; Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, Gower St, London WC1E 6BT, UK.

Water Res ; 247: 120791, 2023 Dec 01.

Article en En | MEDLINE | ID: mdl-37924686

Asunto(s)

Inundaciones; Gestión de Riesgos; Predicción; Factores de Tiempo

Palabras clave

Data mining; Drainage systems; Dynamic ensemble modelling; Real-time modelling; Urban flood forecasting

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Gestión de Riesgos / Inundaciones Idioma: En Revista: Water Res Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido

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