Enhanced water demand analysis via symbolic approximation within an epidemiology-based forecasting framework
Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein de...
| Autor Principal: | Navarrete-López, Claudia |
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| Otros Autores: | Herrera, Manuel, Brentan, Bruno M., Luvizotto, Edevar, Izquierdo, Joaquín |
| Formato: | Generación de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicos |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: |
Navarrete-López, C., Herrera, M., Brentan, B. M., Luvizotto, E., & Izquierdo, J. (2019). Enhanced water demand analysis via symbolic approximation within an epidemiology-based forecasting framework doi:10.3390/w11020246 |
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| Sumario: |
Epidemiology-based models have shown to have successful adaptations to deal with
challenges coming from various areas of Engineering, such as those related to energy use or
asset management. This paper deals with urban water demand, and data analysis is based on
an Epidemiology tool-set herein developed. This combination represents a novel framework in
urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic
approximate (SAX) coding technique able to deal with simple versions of data sets are presented.
Then, a neural-network-based model that uses SAX-based knowledge-generation from various
time series is shown to improve forecasting abilities. This knowledge is produced by identifying
water distribution district metered areas of high similarity to a given target area and sharing
demand patterns with the latter. The proposal has been tested with databases from a Brazilian
water utility, providing key knowledge for improving water management and hydraulic operation of
the distribution system. This novel analysis framework shows several benefits in terms of accuracy
and performance of neural network models for water demand. |
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