Report Cards for Manholes

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 evaluat- ing our machine learning model in three important ways: elimination of outliers, elimination of falsely predictive fea- tures, 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 man- holes. 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 automat- ically assembles a "report card or "profile" highlighting data associated with a given manhole. Prior to the process- ing work that underlies the profiling tool, case studies of a single manhole took several days and resulted in an incom- plete 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 prior- ities for the secondary electrical grid.