Improving quality of water network documentation
The network data and documentation of water companies is often documented poorly with bad quality. KeyAqua is the solution for better documentation.
Locations of ducts and water nodes are converted long time ago from paper maps to different CAD (Computer-Aided Design)/GIS (Geographic Information System) systems and then converted even further to modern network information systems, like KeyAqua. Even though the data might have been accurate in the old maps, quality, attributes, and location information might have been lost after multiple conversions.
Absence of height information, only partly digitized ducts, “double objects” and poor network topology are the most common errors in water distribution network data. E.g. if ducts and valves are in a same layer in a cad file, they will be converted to ducts, not valves.
These problems have significant impact on lot of things, for example duct length reports are useless if the number of digitized ducts is double compared to real existing ducts. Hydraulic modeling and network planning are hard to do if network topology is non-existing or current attributes or locations of network objects and nodes are unknown.
These projects are always started by analyzing the network and attributes. With different database queries it is possible to understand what the overall quality and condition of network data is, for example how many ducts are not connected to nodes or are there any height information for network objects. It is also important to look at the geographical solution itself. How are annotations drawn? Are there some extra objects in the network that should not be there? Based on these results it is possible to find the best solutions together with the customer to improve the quality of network data.
Keypro professional services includes multiple different data quality projects and analysis. Goals for each project are customized based on specific customer needs and data. In these projects goal is to fit existing data quality programs to address these needs. In addition to these automated data quality and analysis processes, also manual fixing is included, where rest of the problems in data quality can be found and fixed manually.
The aim of the various data processing measures is to improve the water utility's network data so that it can be used in a comprehensive way, such as in field operations, management reporting and advanced analyses like hydraulic modeling. Comprehensive and high-quality network information brings wide range of cost benefits to the water utility.
What makes data quality projects interesting is the utilization of different tools and the combination of data. For example, elevation modeling uses open source ground elevation data, which is run with the FME tool directly into the database. After this, various scripts can be used to calculate correct elevations for the pipes, which can be utilized in modeling. You can immediately see how it improves the quality of the network data.
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