Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems

Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new...

Development of an LSTM-based methodology for burst detection in water distribution systems

Water utilities face many challenges, including pipe bursts that cause significant non-revenue water losses. Detecting those bursts early is important for the water sector in its path to achieve sustainable water resource management. This study presents a scalable data-driven methodology for burst detection in water distribution systems that is...