Report cards for manholes: Eliciting expert feedback for a learning task

Abstract

We present a manhole profiling tool, developed as part of the Columbia/Con Edison machine learning project on manhole event prediction, and discuss its role in evaluating our machine learning model in three important ways: elimination of outliers, elimination of falsely predictive features, and assessment of the quality of the model. The model produces a ranked list of tens of thousands of manholes in Manhattan, where the ranking criterion is vulnerability to serious events such as fires, explosions and smoking manholes. Con Edison set two goals for the model, namely accuracy and intuitiveness, and this tool made it possible for us to address both of these goals. The tool automatically assembles a "report card" or "profile" highlighting data associated with a given manhole. Prior to the processing work that underlies the profiling tool, case studies of a single manhole took several days and resulted in an incomplete study; locating manholes such as those we present in this work would have been extremely difficult. The model is currently assisting Con Edison in determining repair priorities for the secondary electrical grid.