Where to Find Water Pipes and Sewers?—On the Correlation of Infrastructure Networks in the Urban Environment

Abstract

Urban water infrastructure, i.e., water supply and sewer networks, are underground structures, implying that detailed information on their location and features is not directly accessible, frequently erroneous, or missing. For public use, data is also not made available due to security concerns. This lack of quality data, especially for research purposes, requires substantial effort when such data is sought for both statistical and model-based analyses. An alternative to gathering data from archives and observations is to extract the information from surrogate data sources (e.g., the street network). The key for such an undertaking is to identify the common characteristics of all urban infrastructure network types and to quantify them. In this work, the network correlations of the street, water supply, and sewer networks are systematically analyzed. The results showed a strong correlation between the street networks and urban water infrastructure networks, in general. For the investigated cases, on average, 50% of the street network length correlates with 80%-85% of the total water supply/sewer network. A correlation between street types and water infrastructure properties (e.g., pipe diameter) cannot be found. All analyses are quantified in the form of different geometric-and graph-based indicators. The obtained results improve the understanding of urban network infrastructure from an integrated point of view. Moreover, the method can be fundamental for different research purposes, such as data verification, data completion, or even the entire generation of feasible datasets.

Enabling Efficient and Sustainable Transitions of Water Distribution Systems under Network Structure Uncertainty

“…Uncertainties in the network structure occur when planning new WDSs [7]. Also in existing WDSs, the exact physical location of network parts (mostly secondary pipes) is sometimes incomplete or even unknown [14]. Furthermore, the variation of future network structure is considered when retrofitting existing systems (e.g., provision of alternative flow paths) to enhance the reliability and robustness of the WDS.…”

“…The loop degree is introduced by the cycle index (CI). It defines the length ratio of alternative paths between two nodes (shortcuts) and the corresponding path obtained from the minimum spanning tree (CI = 0 is a fully branched system, for CI > 0 loops are created) [14].…”

“…(Available online: https://github.com/iut-ibk/DynaVIBe (accessed on 15 August 2017)) The software includes: 1) a spanning tree-based algorithm for network structure design, 2) algorithms for future demand projection and 3) an automated pipe-sizing algorithm to create WDSs based on GIS data [20,21]. Previous studies showed the high colocation of street and water distribution networks [14], which offers the opportunity to use that information for the WDS design. The required input data are the digital elevation map (DEM) of the town, the positions and the supply ratio (%) of the water sources, the street network, the nodal demands and -optional -the known pipe sections (see "set" pipe in Figure 2).…”

“…Pipes with a changing diameter are added to the new pipe set |P up | = {}, representing the necessary pipe upgrades. According to Mair et al [14] the generated WDS set is classified with the cycle index (CI), describing the degree of loops (alternative flow paths) in the network. With this approach sufficiently working "engineering" solutions, rather than optimal WDS are generated with adequate computational capacity.…”

“…In the context of network structure uncertainty, 30 possible WDSs (planning options) with different properties are automatically created by the variation of the cycle index (CI), which defines the degree of alternative flow paths [14]. Two possible planning options with looped and branched WDS structure, generated on basis of the street network, are illustrated in Figure 6b, In the context of network structure uncertainty, 30 possible WDSs (planning options) with different properties are automatically created by the variation of the cycle index (CI), which defines the degree of alternative flow paths [14].…”