Leak Event Identification in Water Systems Using High Order CRF

TL;DR

This paper presents a two-phase system that uses high order CRFs and multi-source data, including IoT sensors and social media, to detect water leaks more accurately and quickly in civil water infrastructure.

Contribution

It introduces a novel high order CRF model integrating IoT, geophysical, and human data for leak detection, with an efficient inference algorithm for improved accuracy.

Findings

Effective leak detection demonstrated in experiments

High order CRF improves prediction accuracy

Integration of social media data enhances detection performance

Abstract

Today, detection of anomalous events in civil infrastructures (e.g. water pipe breaks and leaks) is time consuming and often takes hours or days. Pipe breakage as one of the most frequent types of failure of water networks often causes community disruptions ranging from temporary interruptions in services to extended loss of business and relocation of residents. In this project, we design and implement a two-phase approach for leak event identification, which leverages dynamic data from multiple information sources including IoT sensing data (pressure values and/or flow rates), geophysical data (water systems), and human inputs (tweets posted on Twitter). In the approach, a high order Conditional Random Field (CRF) is constructed that enforces predictions based on IoT observations consistent with human inputs to improve the performance of event identifications. Considering the physical water network as a graph, a CRF model is built and learned by the Structured Support Vector Machine (SSVM) using node features such as water pressure and flow rate. After that, we built the high order CRF system by enforcing twitter leakage detection information. An optimal inference algorithm is proposed for the adapted high order CRF model. Experimental results show the effectiveness of our system.