In response to the pressing need for advanced leak detection in water distribution networks, this research endeavors to develop a sophisticated machine-learning pipeline named H2OArmor. The pipeline is designed to leverage various methods for detecting leakages by utilizing diverse data sources. Crucially, the ensembled opinions of these methods will be intelligently integrated to generate a confidence score for precise event detection.
H2OArmor’s development will be anchored on a robust framework. This framework not only streamlines the implementation of machine learning algorithms but also offers flexibility in onboarding different water utilities. The methodology of the thesis should include multiple machine learning models contributing towards a final informed decision on identifying leak events at DMA level. Furthermore, the thesis scope includes implementation of an end-to-end automated ML Pipeline, which can be used at scale to deploy with minimal manual intervention.
The thesis encompasses several key work packages:
- Framework Implementation: Utilization of a robust ML framework to build the Machine Learning pipeline, ensuring efficiency and compatibility. Either there would be a need to develop such a framework from scratch or there would be utilization of components of a pre-built framework.
- Development of ML-based Methods: Creation of machine learning methods ensuring accuracy and adaptability.
- Automated Onboarding Process: Designing an automated onboarding process for new methods, enhancing the scalability and versatility of H2OArmor as additional techniques are incorporated.
- Scoring Mechanism Development: Creation of a scoring mechanism that synthesizes the ensemble opinions of the various methods, providing a unified confidence score for leak detection events.
H2OArmor aims to revolutionize leak detection in water distribution networks by tailoring its approach to the digital maturity levels of water utilities, ensuring optimal performance and reliability across a spectrum of operational contexts.
[1]Fan, X., Zhang, X. & Yu, X.(.B. Machine learning model and strategy for fast and accurate detection of leaks in water supply network. J Infrastruct Preserv Resil 2, 10 (2021). https://doi.org/10.1186/s43065-021-00021-6