Title:	DATA ORIENTED ALGORITHM FOR REAL-TIME ESTIMATION OF FLOW RATES AND FLOW DIRECTIONS IN A WATER DISTRIBUTION NETWORK
Author(s):	Christophe Dumora, David Auber, Jérémie Bigot, Vincent Couallier and Cyril Leclerc
ISBN:	978-989-8533-80-7
Editors:	Ajith P. Abraham, Jörg Roth and Guo Chao Peng
Year:	2018
Edition:	Single
Keywords:	Graph Theory, Maximum Flow Problem, Data Driven, Sensors, Internet of Things, Water Distribution Network
Type:	Full Paper
First Page:	154
Last Page:	161
Language:	English
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Paper Abstract:	The aim of this paper is to present how data collected from a water distribution network (WDN) can be used to reconstruct flow rate and flow direction all over the network to enhance knowledge and detection of unforeseen events. The methodological approach consists in modeling the WDN and all available sensor data related to the management of such a network in the form of a flow network graph , with a set of nodes, a set of edges whose elements are ordered pairs of distinct nodes, a source node, a sink node and a capacity function on edges. Our objective is to reconstruct a real valued function on all the edges from partial observations on a small number of nodes . This reconstruction method consists in a data-driven Ford-Fulkerson maximum-flow problem in a multi-source, multi-sink context using a constrained bidirectional breadth-first search based on Edmonds-Karp method. The innovative approach is its application in the context of smart cities to operate from sensor data, structural data from geographical information system (GIS) and consumption estimates.
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