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A Process for Predicting Manhole Events in Manhattan

Author(s)
Isaac, Delfina F.; Ierome, Steve; Dutta, Haimonti; Radeva, Axinia; Passonneau, Rebecca J.; Rudin, Cynthia; ... Show more Show less
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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.
Date issued
2010-07
URI
http://hdl.handle.net/1721.1/57432
Department
Sloan School of Management
Journal
Machine Learning
Publisher
Springer Netherlands
Citation
Rudin, Cynthia. et al. "A process for predicting manhole events in Manhattan." Mach Learn (2010) 80: 1–31
Version: Author's final manuscript
ISSN
0885-6125
1573-0565
Keywords
Manhole events, Applications of machine learning, Ranking, Knowledge discovery

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