A Process for Predicting Manhole Events in Manhattan
Name
Rudin_A Process for Predicting.pdf
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743.32 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Isaac, Delfina F.
Ierome, Steve
Dutta, Haimonti
Radeva, Axinia
Passonneau, Rebecca J.
Rudin, Cynthia
Date Issued
July 2010
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
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.
Subjects
Manhole events
Applications of machine learning
Ranking
Knowledge discovery
MIT Department
Sloan School of Management
Terms of Use
Attribution-Noncommercial-Share Alike 3.0 Unported
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-009-5166-y
