Resumen: [EN] Leak detection and isolation (LDI) is a problem of interest for water management companies
and their technical staff. Main reasons for this are that early detection of leakages can reduce
dramatically (1) water losses in urban networks and (2) the environmental burden due to wasted
energy used in the system supply [1]. Water leakage can become a very complex problem,
due to the lack of information about the water system and because a leak might not be easily
detected on-sight. Therefore, any diagnostic tool that could help in such task are valuable for
engineers and managers. Soft computing tools have shown to be valuable tools for researchers
in different fields. Supervised machine learning techniques for example, have been used with
success in complex problems, for binary and multi class classification. This is useful in order
to detect different faulty scenarios in complex systems using for example, on-line data from
SCADA systems. In this paper, we provide an analysis on some soft computing techniques used
for LDI in urban networks. This with the aim of identifying strengths and drawbacks among
different machine learning techniques for this task in real-time acquisition scenarios.
