Using machine learning to identify real emergency alarm events

In a very interesting presentation at HxGNLIve in Las Vegas, Chris Klimmm, a subject matter expert with Hexagon Safety and Infrastructure, reported using Microsoft Azure's machine learning capability to develop a model to winnow out likely real burglar alarm events from the 97% of false alarms.

I have blogged about a number of applications involving machine learning.  There are tools on the major cloud service providers for creating and running models.  The ones on Microsoft Azure in particular appear to be relatively easy to use.  The challenge is finding training and testing data.  In this case Hexagon has a mobile response application Called InService that is used to respond to burglar alarm events.  Working with their customers provided validated training data that could be used to train and test a machine learing model which HexagonSI reportedly did quite successfully.

An interesting application of this approach for utilities would be to a social media stream such as twitter to help utilities identify valid tweets that were evidence of outages or other utility-related issue using spatial proximity and keywords to identify candidate tweets.