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A proposal of optimal sampling design using a modularity strategy
Author(s) -
Simone A.,
Giustolisi O.,
Laucelli D. B.
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2016wr018944
Subject(s) - modularity (biology) , sampling (signal processing) , optimal design , metric (unit) , computer science , network planning and design , data mining , mathematical optimization , reliability engineering , engineering , mathematics , machine learning , computer network , operations management , genetics , filter (signal processing) , computer vision , biology
In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN‐oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design , i.e., the optimal location of pressure meters , using newly developed sampling‐oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling‐oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.