A process for predicting manhole events in Manhattan

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 Editor: Carla Brodley.This work was done while Cynthia Rudin was at

Reactive point processes: A new approach to predicting power failures in underground electrical systems

“…As an alternative method for estimating 0 in a personalized way, one can use a longterm probabilistic model (such as the one we had developed for the project previously in Rudin et al, 2010) that incorporates features for each manhole.…”

Interactions between learning and decision making

“…Machine learning models have started to be used for proactive maintenance in NYC, where supervised ranking algorithms are used to rank the manholes in order of predicted susceptibility to failure (fires, explosions, smoke) so that the most vulnerable manholes can be prioritized [Rudin et al, 2010[Rudin et al, , 2012a. The machine learning algorithms make reasonably accurate predictions of manhole vulnerability; however, they do not (nor would they, using any other prediction-only technique) take the cost of repairs into account when making the ranked lists.…”

“…In New York City, there are several separate new preemptive maintenance programs, including the targeted inspection program for electrical service structures (manholes), programs that perform extensive repairs that were placed on a waiting list after the manhole was inspected, and the vented cover replacement program, where each manhole is replaced with a vented cover that allows gases to escape, mitigating the possibility and effects of serious events including fires and explosions. Con Edison, the power company in NYC, has the ability to use machine learning models in Manhattan, Brooklyn and the Bronx for scheduling of manhole inspection and repair work [Rudin et al, 2010[Rudin et al, , 2012b. This project was the motivation for the development of the ML&TRP and we use data from the NYC power grid for our experiments.…”

Machine learning for science and society

“…We may prevent truly new applications of ML to be published in top venues at all (ML or not). For instance, one of the editors of this special issue uses ML to predict manhole fires and explosions on the NYC power grid (Rudin et al 2010. There is no existing applied journal that would be a natural fit for this work, since it is a novel application.…”

“…The editors of this special issue have worked on both theoretical and applied topics, where the applied topics between us include criminology , crop yield prediction (Wagstaff et al 2008), the energy grid (Rudin et al 2010, healthcare (Letham et al 2013b;McCormick et al 2012), information retrieval (Letham et al 2013a), interpretable models (Letham et al 2013b;McCormick et al 2012;Ustun et al 2013), robotic space exploration (Castano et al 2007;Wagstaff and Bornstein 2009;Wagstaff et al 2013b), and scientific discovery (Wagstaff et al 2013a). In our experience, working in applied areas strongly motivates the development of algorithms and theory that can go beyond the single application domain for which they were designed.…”