Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow Directions in a Water Distribution Network

TL;DR

This paper introduces a data-driven algorithm that reconstructs flow rates and directions in water distribution networks in real-time, leveraging sensor data and graph modeling to improve event detection and network management.

Contribution

It presents a novel application of a maximum-flow algorithm combined with GIS data for real-time flow estimation in water networks, enhancing existing methods.

Findings

Effective real-time flow reconstruction demonstrated

Improved detection of unforeseen events in water networks

Integration of sensor and GIS data enhances accuracy

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 G = (V, E, s, t, c), with V a set of nodes, E a set of edges whose elements are ordered pairs of distinct nodes, s a source node, t a sink node and c a capacity function on edges. Our objective is to reconstruct a real-valued function f(u,v): VxV => R on all the edges E in VxV from partial observations on a small number of nodes V = {1, ..., n}. 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 a geographical information system (GIS) and consumption estimates.