  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.
