In order to recognise the quality of research presented in Water Supply, last year we introduced an annual Best Paper Award.

To qualify for this award, the paper must be included in one of the issues from that publication year. After a long list was created from Editor Recommendations and overall scores, we were able to create a short list of papers which were then sent to the following Water Supply Editors: Ricardo Cobacho, Giovanni De Feo, Jiangyong Hu and Regina Sommer who assessed the papers in regards to scientific excellence, relevance to practice, and overall presentation.

The winner of this award, will receive the following: a book voucher entitling them to an IWA Publishing book of their choice and their winning paper will also be made Open Access. 

The Water Supply Best Paper Award for 2019 goes to Bas A. Wols, Ad Vogelaar, Andreas Moerman and Bernard Raterman from KWR Watercycle Research Institute in The Netherlands, with B.A. Wols, also affiliated with Wetsus in the Netherlands for their contribution entitled:

‘Effects of weather conditions on drinking water distribution pipe failures in the Netherlands’

Water Supply, 19(2): 404-416


In this paper – published in 2019 Volume 19 Issue 2 of Water Supply –  advanced data mining techniques were elegantly employed to analyse and quantify how diverse types of information impact on pipe failure. The authors used comprehensive sets of failure data, weather variables, satellite images of soil settling and pressure variations generated by hydraulic modelling, combined with a thoughtful way of grouping of pipes in cohorts. The results were clearly presented and they are updateable as more data get collected. This approach is applicable in other countries where similar data sets exist. The practical value of this methodology is in its potential for identification of weak pipes, which enables a better informed proactive management of pipe networks. 

We would also like to give an honourable mention to our runner up paper: ‘Detection of multiple leakage points in water distribution networks based on convolutional neural networks’ by QianSheng Fang, JiXin Zhang, ChenLei Xei and YaLong Yang from Jianzhu University, China.


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