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Water 2019, 11(2), 246; https://doi.org/10.3390/w11020246

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

1
Faculty of Environmental Engineering, Universidad Santo Tomás, Bogotá 110231, Colombia
2
Institute for Manufacturing—Department of Engineering, University of Cambridge, Cambridge CB3 0FS, UK
3
LHC—Faculty of Civil Engineering, Universidade Estadual de Campinas, Campinas 13083-970, Brazil
4
Fluing—Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Received: 28 December 2018 / Revised: 24 January 2019 / Accepted: 26 January 2019 / Published: 31 January 2019
(This article belongs to the Section Urban Water Management)
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Abstract

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. View Full-Text
Keywords: water distribution systems; epidemiology; time series analysis; pattern recognition; dimension reduction water distribution systems; epidemiology; time series analysis; pattern recognition; dimension reduction
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Navarrete-López, C.; Herrera, M.; Brentan, B.M.; Luvizotto, E., Jr; Izquierdo, J. Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water 2019, 11, 246.

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