Systematic generation of virtual networks for water supply

Abstract

[1] Building theories from case studies is a common research approach that can also be applied to the analysis of networks. Although case studies of real systems bridge the gap between theory and practice, they are nevertheless investigations of a subset of cases, each with specific characteristics. To tackle this problem, a multitude of networks with diverging characteristics are generated using the graph-theory-based Modular Design System (MDS). In this paper the application of this MDS is demonstrated by generating a set of 2280 virtual Water Supply Systems. The layout and the properties of these systems are representative of typical examples encountered in practice. Scatterplots, density, and cumulative distribution functions are used to characterize several network parameters. A comparison of the virtual set with three real-world case studies shows similar characteristics. Finally, the potential of the methodology is demonstrated by analyzing the impact of increased water demand on hydraulic performance. It can be shown that the allowed maximum and minimum diameters envelop a range of impacts on mean values for nodal pressure.

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

“…Consequently, large networks are not often considered by optimal design studies. This situation can be remedied by using latest developments in methods capable of generating realistic WDS networks by [55][56][57], who have each developed their own automatic network generation software. Real-world WDS design optimisation problems normally involve large size, complex-topology networks, comprising a number of elements of various types.…”

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

“…by using various optimization methods (running on the same computation platform in order to compare the execution times) are currently quite limited. Although some artificial generators [147], [148] exist to construct virtual water networks in a certain level of complexity, this does not remove the necessity for constructing central repositories of WDNs and WWCNs for the convenience and fairness of comparisons between diverse optimization methodologies. Similar phenomena can also be seen in the field of machine learning where several central repositories exist, for example, the well-known UCI (University of California, Irvine) machine learning repository [149] despite the fact that various artificial sampling data generators exist such as the time-series prediction data generator.…”

Spanning Tree-Based Algorithm for Generating Water Distribution Network Sets by Using Street Network Data Sets

“…In literature, systematic generation of virtual water supply systems is shown by e.g. Möderl et al (2011) or Sitzenfrei et al (2011). A short-coming of these approaches is that the layouts of the created water distribution models are very conceptual and not realistic (orthogonal network layout).…”