A Proposal of Optimal Sampling Design Using Infrastructure Modularity

Abstract Planning of pressure observations/meters in terms of spatial distribution and number is named sampling design. In the past, the hydraulic model calibration was the main driver of the sampling design. Today, water utilities are interested in system pressure monitoring for hydraulic system analysis and management with respect to other technical purposes as for example detection of anomalies (burst leakages and anomaly head losses) and service quality with respect to customers. In recent years, the optimal location of flow observations/meters, related to design of optimal district metering areas, has been faced considering optimal network segmentation and the modularity index using a multi-objective strategy. The original modularity index from the studies of the complex network theory was transformed to be WDN-oriented. Consistently, this paper proposes a new way to perform the sampling design using newly developed sampling-oriented modularity-based metrics. The strategy optimizes the location of the pressure meters based on network topology creating pressure district metering areas, i.e. it returns the optimal location of the nodal pressure meters defining “pressure DMAs”. The multi-objective optimization problem minimizes the cost of newly installed meters while maximizing the sampling-oriented modularity metrics. The battle of background leakages assessment water network (BBLAWN) allows presenting and discussing the proposed sampling design methodology.

[1]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[2]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[3]  James Davidson,et al.  Real-Time Connectivity Modeling of Water Distribution Networks to Predict Contamination Spread , 2005 .

[4]  Jochen Deuerlein,et al.  Decomposition Model of a General Water Supply Network Graph , 2008 .

[5]  Luigi Berardi,et al.  Operational and Tactical Management of Water and Energy Resources in Pressurized Systems: Competition at WDSA 2014 , 2016 .

[6]  Carlo Giudicianni,et al.  Water Distribution System Clustering and Partitioning Based on Social Network Algorithms , 2015 .

[7]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Paul Jeffrey,et al.  Water distribution system vulnerability analysis using weighted and directed network models , 2012 .

[9]  Avi Ostfeld,et al.  Topological clustering for water distribution systems analysis , 2011, Environ. Model. Softw..

[10]  Nien-Sheng Hsu,et al.  Water Distribution Network Reliability: Connectivity Analysis , 1996 .

[11]  I. C. Goulter,et al.  Evaluation of methods for decomposition of water distribution networks for reliability analysis , 1988 .

[12]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Orazio Giustolisi,et al.  New Modularity-Based Approach to Segmentation of Water Distribution Networks , 2014 .

[14]  Wolfgang Rauch,et al.  Automated Creation of District Metered Area Boundaries in Water Distribution Systems , 2013 .

[15]  Richard A. Brualdi,et al.  Combinatorial matrix theory , 1991 .

[16]  Orazio Giustolisi,et al.  A novel infrastructure modularity index for the segmentation of water distribution networks , 2014 .

[17]  T. Walski.  Technique for Calibrating Network Models , 1983 .

[18]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[19]  Roberto Revelli,et al.  Community detection as a tool for complex pipe network clustering , 2013 .

[20]  Stefano Alvisi,et al.  A heuristic procedure for the automatic creation of district metered areas in water distribution systems , 2014 .

[21]  Luigi Berardi,et al.  General metrics for segmenting infrastructure networks , 2015 .