By Shervin Khazaeli, PhD and Katie Deheer, MS, MBA
As water systems continue to work towards achieving Lead & Copper Rule Revisions (LCRR) compliance, they might consider using predictive modeling to reduce unknowns in their inventory and find lead service lines more efficiently. Predictive modeling can be a powerful tool to support water systems on their LCRR compliance journey, but to be reliable, predictive models need to be built on representative data that does not contain bias. For example, building a model with field verifications only from a certain neighborhood or from homes built during a certain decade would be biased. Unbiasing your predictive model begins with getting a data set of field verifications representative of the larger water system. But don’t worry… if you already have some field verifications from prior fieldwork, you don’t necessarily need to start from zero.