Midas: Multiple Data Sources

Background

Drinking water utilities and Water Authorities increasingly wish to support their decisions concerning the maintenance and rehabilitation of their pipes with detailed condition models. Recent joint research by the Dutch and Flemish drinking water utilities has developed models of this kind (see, for example, Wols, Moerman and Vertommen, 2015; Wols and Moerman, 2017; Van Laarhoven and Van Vossen, 2019). Their application however makes it increasingly clear that the successful use of such models depends fundamentally on the availability of input data that describe the pipe condition in a sufficiently detailed and reliable manner. It also became clear that the availability of such data has so far been limited. The relevant model data can be obtained by means of inspections, but this requires a major effort. It is therefore important to prioritise inspections as much as possible according to their potential impact on decisions (measuring the most important parameters first), and to make the best possible use of additional data [company or public information, such as soil maps, road maps, KNMI (Royal Netherlands Meteorological Institute) data] to supplement the available direct measurements. It is also important to incorporate the correct level of certainty or reliability of these parameters into the processing and use of the data.

Objective

The objective of this project is to improve the availability of data for use in pipe-condition models, and to provide insight into the importance of individual data components (and their uncertainty) for the results of these models. The application of such condition models depends fundamentally on disposing of the right data. The project’s outcomes will offer the Water Authorities and drinking water utilities the possibility of collecting these data as efficiently as possible (only purchasing/measuring the important data). By supporting the use of condition models, this project’s outcomes will give Water Authorities and drinking water utilities access to better-founded, more precise estimates of the failure probabilities of their pipes, which they can use to support their pipe rehabilitation decisions. This will minimise costs by avoiding needless investments (rehabilitating only the right pipes), and also minimise the costs and nuisance of failures (rehabilitating failure-sensitive pipes first). The outcomes will also offer technology providers the possibility in the coming years of applying condition models in a well-founded manner when interpreting environmental data and inspections for their clients, and of developing targeted inspections to fill in any existing data gaps.