Predictive Modeling

The use of advanced artificial intelligence (AI) and machine learning algorithms to analyze data and predict the likelihood of lead service lines (LSLs) being present. This technology helps utilities identify areas where it is more likely to encounter LSLs and make informed decisions about where to prioritize replacements.

« Back to Glossary
Share
Recent Resources
Copper lines
  • Case Study
  • Guidance

    • Requiring LSL Replacement WhenOpportunities Arise
    Columbus Skyline
  • Announcement

    • H2Ohio Invests $500,000 in Columbus to Replace Lead Lines at Childcare Facilities
    Wausau celebrated its 1,000th lead service line replacement after completing the milestone in just over one year.
  • Case Study

    • City of Wausau and Community Infrastructure Partners Celebrate 1,000th Lead Service Line Replacement, Set National Model for Lead-Free Future

    Milwaukee is one of the few cities in the country with a prioritization plan to ensure neighborhoods likely to suffer the most severe impacts from lead poisoning get their pipes replaced first. In consultation with a community-based group, Coalition for Lead Emergency (COLE), and following a public engagement process, Milwaukee included in an ordinance three indicators to prioritize where LSLs will be removed first:

    1. The area deprivation index (ADI), which is a compilation of social determinants of health
    2. The percentage of children found to have elevated lead levels in their blood when tested for lead poisoning
    3. The density of lead service lines in the neighborhood.

    Read more here.