CUED Publications database

Water supply clusters based on a boosting semi-supervised learning methodology

Herrera, M and Izquierdo, J and Pérez-García, R and Montalvo, I (2010) Water supply clusters based on a boosting semi-supervised learning methodology. In: UNSPECIFIED.

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Abstract

Division of Water Supply Networks (WSN) into isolated supply clusters is used in most of the big cities around the world and aims at improving the management of the whole system. In this paper we propose a solution to perform this partition by using graphical information and supply constraints through a semi-supervised spectral clustering algorithm. This methodology has some difficulties in large-scale problems. We approach it by a boosting method based in multi-agent subgraph exploration. The proposal, tested in a real network, allows us to conclude that we have obtained a robust methodology, able to partition a large size WSN. © 2010 Civil-Comp Press.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div E > Manufacturing Systems
Depositing User: Cron Job
Date Deposited: 18 May 2020 20:02
Last Modified: 10 Dec 2020 08:07
DOI: