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How to Evaluate Predictive Models for Lead Service Line Inventory and Replacement

The EPA has included predictive modeling as a service line material investigation method. The approach sounds straightforward: use the information you know to make predictions about what you don’t yet know. 

If you are not a trained data scientist, how do you pick an approach? This guide will cover what predictive modeling is and give you seven questions to ask to help you choose wisely.

What is Predictive Modeling for Lead Service Lines?

Utilities do not have complete or even accurate service line materials records. In the absence of comprehensive records, utilities have turned to data science to predict unknown service line materials. They can then use those predictions to prioritize lines for excavation and replacement.

A predictive model uses known information to predict what is unknown: service line materials in this case.

A model can use many inputs, including the utility’s historical records, and information about the local built environment such as build year, zoning, location, demographics, information on nearby infrastructure like fire hydrants, and local water samples.

The process is iterative: Where lead is and is not found guides the model to make better predictions.

Get the full guide to evaluating predictive models for lead service line inventory and replacement.

About BlueConduit

BlueConduit originated the approach of using machine learning to predict lead service line materials and have been doing it longer than anyone else. Our team has been helping local officials and their engineering partners identify and remove lead service lines since 2016.

In 2020, BlueConduit co-authored a white paper with the Association of State Drinking Water Administrators on “Principles of Data Science for Service Line Inventory and Replacement.”

We’re now streamlining the predictive modeling process for our utility and engineering customers.

BlueConduit enables utilities to focus their resources on digging where the lead is to accelerate the removal of this significant health concern and save millions of dollars in avoided digs.

Jared Webb

About Jared Webb

Jared Webb is BlueConduit’s Chief Data Scientist. His responsibilities include processing and analyzing customer data, managing relationships with technical service partners, and producing output of Machine Learning results. He has been a member of Dr. Schwartz and Dr. Abernethy’s team since 2016 and has served as Chief Data Scientist since the formation of BlueConduit. Jared received his Undergraduate and Masters in applied mathematics from Brigham Young University, where he focused on the mathematical foundations of machine learning models.

Dunrie Greiling

About Dunrie Greiling

Dunrie Greiling is BlueConduit’s VP of Marketing. She combines her training in science (biology Ph.D., University of Michigan) with her two decades of experience in tech startups. Prior to joining BlueConduit, she worked in science-based software and device companies in many roles, as an employee, as a consultant, and as an advisor and entrepreneur-in-residence.