From GIS to Graphical Representation for Maintaining Connectivity of Wastewater Network Elements

Abstract Wastewater network management is a critical aspect of ensuring public health and environmental sustainability. Geographic Information Systems (GIS) are widely employed for managing and analyzing wastewater network data. A Shapefile is a model for representing vector data in GIS. In this model, different databases are used to store various types of geometric data, including points, lines, and polygons. For instance, the components of wastewater networks are stored in different databases. However, this approach presents difficulties in accurately depicting the connectivity among the wastewater network components that are physically interconnected in reality. To address this issue, this paper discusses the limitations of the separate databases approach for representing wastewater networks in Shapefile and proposes a novel graph-based approach. In this approach, each component of the network, such as manholes, structures, pumps, etc., is represented as a node in the novel graph, while the pipes represent the connections between them. By adopting the proposed representation, the real interconnected nature of the wastewater network can be effectively captured and visualized. The validation of this approach, using five real datasets, confirms its ability to connect the various components of the wastewater network via a graph-based representation.

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Publication Details

Journal
SN Computer Science
Published
2024-09-02
DOI
https://doi.org/10.1007/s42979-024-03174-9
Citations
2
Primary Topic
Data Management and Algorithms
Type
article
Field-Weighted Citation Impact
0.70

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article

From GIS to Graphical Representation for Maintaining Connectivity of Wastewater Network Elements

Thanh‐Nghi Do, Carole Delenne, Truong-Thanh Ma, Omar Et-targuy et al.
2 citations
SN Computer Science
Data Management and Algorithms
0.70
article

From GIS to Graphical Representation for Maintaining Connectivity of Wastewater Network Elements

Thanh‐Nghi Do, Carole Delenne, Truong-Thanh Ma, Omar Et-targuy, Ahlame Begdouri, Salem Benferhat
article en
2 citations

Abstract

Abstract Wastewater network management is a critical aspect of ensuring public health and environmental sustainability. Geographic Information Systems (GIS) are widely employed for managing and analyzing wastewater network data. A Shapefile is a model for representing vector data in GIS. In this model, different databases are used to store various types of geometric data, including points, lines, and polygons. For instance, the components of wastewater networks are stored in different databases. However, this approach presents difficulties in accurately depicting the connectivity among the wastewater network components that are physically interconnected in reality. To address this issue, this paper discusses the limitations of the separate databases approach for representing wastewater networks in Shapefile and proposes a novel graph-based approach. In this approach, each component of the network, such as manholes, structures, pumps, etc., is represented as a node in the novel graph, while the pipes represent the connections between them. By adopting the proposed representation, the real interconnected nature of the wastewater network can be effectively captured and visualized. The validation of this approach, using five real datasets, confirms its ability to connect the various components of the wastewater network via a graph-based representation.

SN Computer ScienceVol. 5(7)
Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), Can Tho University (VN), Université de Montpellier (FR), Laboratoire HydroSciences Montpellier (FR), Centre de Recherche en Informatique (FR), Institut de Recherche pour le Développement (FR), Université d'Artois (FR), Sidi Mohamed Ben Abdellah University (MA)
European Commission, Agence Nationale de la Recherche, H2020 Marie Skłodowska-Curie Actions, HORIZON EUROPE Marie Sklodowska-Curie Actions
Openalex Percentile: Top 32%
Data Management and Algorithms
0.70
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