Integration and Alignment of Multiple Water Network Data Sources

Wastewater network management relies on geographic data from multiple sources, which creates significant integration challenges: spatial inconsistencies, incomplete coverage, and varying levels of precision.

Although different data sources may cover the same portion of the network, they are generally produced in different contexts or at different times. This can result in discrepancies in the descriptions of the physical infrastructure of the wastewater network: some elements may be accurately represented in one source but absent in another, while other objects may be described slightly differently across sources. Furthermore, for certain parts of the network, the structure itself may vary depending on the source. Consequently, any operation to merge datasets or build a global network representation requires matching the objects described by each source in order to identify those corresponding to the same physical element, to recognize objects present in multiple sources, and to distinguish those with no correspondence in other datasets.

In this work, we propose a data integration methodology to address disparities among these data sources and to match the various elements of wastewater networks. This approach establishes correspondences between multiple datasets representing the same infrastructure from different sources. By combining spatial and structural information, the method identifies matching components across datasets and produces a unified representation that leverages the complementary information from each source while resolving conflicts and inconsistencies.

The approach has been validated on real-world wastewater network data from multiple sources and covering different time periods. The results demonstrate high integration accuracy. This methodology enables a complete and consistent representation of wastewater networks, addressing the challenges of data heterogeneity inherent in multi-source infrastructure management.