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With sustainable development as their overarching goal, Urban Water System (UWS) managers need to take into account all social, economic, technical and environmental facets related to their decisions. Decision support systems (DSS) have been used widely for handling such complexity in water treatment, having a high level of popularity as academic exercises, although little validation and few full-scale implementations reported in practice. The objective of this paper is to review the application of artificial intelligence methods (mainly machine learning) to UWS and to investigate the integration of these methods into DSS. The results of the review suggest that artificial neural networks is the most popular method in the water and wastewater sectors followed by clustering. Bayesian networks and swarm intelligence/optimization have shown a spectacular increase in the water sector in the last 10 years, being the latest techniques to be incorporated but overtaking case-based reasoning. Whereas artificial intelligence applications to the water sector focus on modelling, optimization or data mining for knowledge generation, their encapsulation into functional DSS is not fully explored. Few academic applications have made it into decision making practice. We believe that the reason behind this misuse is not related to the methods themselves but rather to the disassociation between the fields of water and computer engineering, the limited practical experience of academics, and the great complexity inherently presentThe authors would like to acknowledge the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007e2013 under REA agreement 289193 (SANITAS ITN). The authors thank the Spanish Ministry of Economy and Competitiveness (RYC-2013-14595, CTM2015-66892-R). The authors also acknowledge support from the Economy and Knowledge Department of the Catalan Government through the Consolidated Research Group (2014 SGR 291) - Catalan Institute for Water Research and (2014-SGR-1168)-LEQUIA-University of Giron

Do machine learning methods used in data mining enhance the potential of decision support systems? A review for the urban water sector