Enabling low-cost automatic water leakage detection: a semi-supervised, autoML-based approach

ABSTRACT

An important aspect of proper management of water resources is the reduction of losses in urban water distribution. Water loss is especially challenging in developing countries such as Brazil. The real-time monitoring of the distribution system followed by the application of outlier detection techniques on water flow data has been an effective strategy to reduce loss. However, these solutions require high investments in specialized personnel for building the models and data collection for machine learning. This work presents a semi-supervised application of outlier detection techniques and Automated Machine Learning (AutoML) resources on water flow data from District Metering Areas (DMAs). The system does not require experts for model configuration nor curated data for training. The system aims at reducing implementation and deployment costs related to (i) hiring machine learning experts for model configuration and (ii) curation of data for model training, enabling a low-investment deployment suitable for low-income regions.

KEYWORDS:

• Leakage detection
• model selection and optimization
• AutoML
• self-organizing Maps
• Local Outlier Factor

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. https://www.ge.com/digital/applications/proficy-historian