Estimation of fuzzy anomalies in Water Distribution Systems

TL;DR

This paper introduces a neural network-based approach for estimating and diagnosing anomalies in Water Distribution Systems by combining optimization, fuzzy estimation, and uncertainty quantification.

Contribution

It presents a novel method integrating fuzzy state estimation with neural networks for anomaly detection in water systems, enhancing diagnosis accuracy.

Findings

Neural network effectively detects anomalies in WDS.

Fuzzy estimation improves uncertainty quantification.

Method outperforms traditional anomaly detection techniques.

Abstract

State estimation is necessary in diagnosing anomalies in Water Demand Systems (WDS). In this paper we present a neural network performing such a task. State estimation is performed by using optimization, which tries to reconcile all the available information. Quantification of the uncertainty of the input data (telemetry measures and demand predictions) can be achieved by means of robust estate estimation. Using a mathematical model of the network, fuzzy estimated states for anomalous states of the network can be obtained. They are used to train a neural network capable of assessing WDS anomalies associated with particular sets of measurements.