Hybrid SOM+k-Means clustering to improve planning, operation and management in water distribution systems. (August 2018)

• Record Type: Journal Article
• Authors: Brentan, Bruno; Meirelles, Gustavo; Luvizotto, Edevar; Izquierdo, Joaquin
• Is Part Of: Environmental modelling & software. Volume 106(2018)
• Page Start: 77
• Page End: 88
• Publication Date: 2018-08
• Subjects: Water supply systems -- Classification -- Self-organizing maps -- k-means clustering; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.02.013
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results. Highlights: Clustering through a hybrid SOM+k-means approach to guide clustering granulation. Three water supply case-studies validate the proposed methodology. The obtained results are useful to aid specific decision-making. Water utility planning, operation and management may benefit from this technique.