Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks

TL;DR

This paper introduces an unsupervised, real-time framework using LSTM-VAE and drift detection to identify pipe blockages and leakages in water networks, effectively handling non-stationary data and limited labels.

Contribution

It presents a novel online, unsupervised detection method combining LSTM-VAE with drift detection for fault identification in water distribution networks.

Findings

Outperforms baseline methods in anomaly detection accuracy.

Effectively adapts to recurrent concept drift in WDNs.

Enables real-time, edge-level fault monitoring.

Abstract

Water Distribution Networks (WDNs), critical to public well-being and economic stability, face challenges such as pipe blockages and background leakages, exacerbated by operational constraints such as data non-stationarity and limited labeled data. This paper proposes an unsupervised, online learning framework that aims to detect two types of faults in WDNs: pipe blockages, modeled as collective anomalies, and background leakages, modeled as concept drift. Our approach combines a Long Short-Term Memory Variational Autoencoder (LSTM-VAE) with a dual drift detection mechanism, enabling robust detection and adaptation under non-stationary conditions. Its lightweight, memory-efficient design enables real-time, edge-level monitoring. Experiments on two realistic WDNs show that the proposed approach consistently outperforms strong baselines in detecting anomalies and adapting to recurrent drift, demonstrating its effectiveness in unsupervised event detection for dynamic WDN environments.