Management of Water Distribution Network Data - Approach and Application in an Algerian City

Water distribution networks are dynamic technical systems changing constantly due to maintenance works and network extensions. A basic requirement for all tasks aiming to assure a reliable water supply is the detailed knowledge of the dynamically changing infrastructure and the hydraulic system reactions.

Therefore, suitable data management concepts for the documentation of all necessary system data and secondary data are needed which consider these dynamics. The data management must comprehend a solution for data storage and provision as well as work procedures for data acquisition and database update.

The technical characteristics of the system elements as well as the system’s topology and the spatial reference are commonly documented in so called network registers. The provision of additional geographical, political and social data of the supply area might also be of use in system analysis and planning. Due to their spatial reference, both system data and additional data can nowadays be managed together in a geographic information system (GIS). This specific GIS is also called network information system (NIS).

In addition, tools for the management of customer data, statistical data of network failures and hydraulic in-time data (Supervisory Control and Data Acquisition Systems) can be connected to or even integrated into the GIS. As system data, spatial data, operational data and consumption data are input parameters for hydraulic system analysis, the connection or integration of hydraulic calculation models is also possible. However, the documentation and update of the system data constitutes the core of every network data management.

In developing countries, infrastructural and cadastral data are often deficiently managed, regardless of whether they are hold available in hardcopy or in digital form. Poor and unreliable databases are the consequence. System information is often even limited to the personal knowledge of a few employees. The risk of losing information or not having it available when needed is obvious. In addition, the data management tools are rarely used efficiently, if at all. One of the many reasons is that the benefits are simply not known or not perceived. If they are, the lack of the required knowhow is a major constraint for the successful application of data management tools. Furthermore, there is a general lack of tools and workflows adapted to the specific institutional, financial and technical boundary conditions.

 

 

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