SAM-kNN Regressor for Online Learning in Water Distribution Networks

TL;DR

This paper introduces an adaptive residual-based anomaly detection system for water distribution networks using an incremental SAM-kNN regressor, capable of handling various changes and anomalies for continuous monitoring.

Contribution

It adapts the incremental SAM-kNN classifier for regression to improve anomaly detection in complex water networks, enabling better adaptation to changing conditions.

Findings

Effective detection of leakages and sensor faults.

Adaptability to various anomalies and network changes.

Enhanced continuous monitoring capabilities.

Abstract

Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at all times, the water supply company continuously monitors the network and takes actions when necessary -- e.g. reacting to leakages, sensor faults and drops in water quality. Since real world networks are too large and complex to be monitored by a human, algorithmic monitoring systems have been developed. A popular type of such systems are residual based anomaly detection systems that can detect events such as leakages and sensor faults. For a continuous high quality monitoring, it is necessary for these systems to adapt to changed demands and presence of various anomalies. In this work, we propose an adaption of the incremental SAM-kNN classifier for regression to build a residual based anomaly detection system for water distribution networks that is able to adapt to any kind of change.