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 devel...

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Bibliographic Details
Published in:Water (Basel) 2019-01, Vol.11 (2), p.246
Main Authors: Navarrete-López, Claudia, Herrera, Manuel, Brentan, Bruno, Luvizotto, Edevar, Izquierdo, Joaquín
Format: Article
Language:English
Subjects:
Accuracy
Adaptation
Approximation
Asset management
Computational fluid dynamics
Data analysis
Economic forecasting
Energy consumption
Epidemiology
Fluid flow
Forecasting
Genomes
Hydraulics
Machine learning
Methods
Model accuracy
Neural networks
Time series
Trends
Water demand
Water distribution
Water distribution systems
Water engineering
Water management
Water supply
Water utilities
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Summary: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.
ISSN:2073-4441
2073-4441
DOI:10.3390/w11020246