A process for predicting manhole events in Manhattan

Authors:
Cynthia Rudin;Rebecca J. Passonneau;Axinia Radeva;Haimonti Dutta;Steve Ierome;Delfina Isaac
Affiliations:
MIT Sloan School of Management, E53-323, Massachusetts Institute of Technology, Cambridge, USA 02139;Center for Computational Learning Systems, Columbia University, New York, USA 10115;Center for Computational Learning Systems, Columbia University, New York, USA 10115;Center for Computational Learning Systems, Columbia University, New York, USA 10115;Consolidated Edison Company of New York, New York, USA 10003;Consolidated Edison Company of New York, New York, USA 10003
Venue:
Machine Learning
Year:
2010

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Abstract

We present a knowledge discovery and data mining process developed as part of the Columbia/Con Edison project on manhole event prediction. This process can assist with real-world prioritization problems that involve raw data in the form of noisy documents requiring significant amounts of pre-processing. The documents are linked to a set of instances to be ranked according to prediction criteria. In the case of manhole event prediction, which is a new application for machine learning, the goal is to rank the electrical grid structures in Manhattan (manholes and service boxes) according to their vulnerability to serious manhole events such as fires, explosions and smoking manholes. Our ranking results are currently being used to help prioritize repair work on the Manhattan electrical grid.