Corralling Machine Learning for LCRR Inventory Development

Corralling Machine Learning For LCRR Inventory Development

on-demand webinar recorded december 2022

This presentation will cover how to responsibly leverage machine learning for service line inventory development. Mark Zito from Trinnex will review the technology development side and cover the data modeling process, organizing data, and reviewing outputs for accuracy. Joanna Cummings from CDM Smith will cover how these machine learning models applied to project planning and field work. We will also cover proposed guidelines for using machine learning.

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At the conclusion of this course, the learners will be able to …

Understand how to leverage machine learning in inventory development and maintenance
Learn how to review outputs for accuracy
Show how the tool works in action
Review how these models tie into project planning and field work
Learn proposed guidelines for responsibly using machine learning that regulators can consider

Don't have time to watch the full webinar or want a refresher? Check out this recap article that highlights the main talking points.

Joanna Cummings, PE is the Lead and Copper Rule Compliance Coordinator at CDM Smith. She has over a dozen years of professional experience in the design and optimization of drinking water process systems. She currently is helping utilities in the Midwest and around the country comply with the lead and copper rule revisions, including corrosion optimization studies, and developing lead service line inventories and replacement plans.

Mark Zito, CFM, GISP, is a leadCast product manager for Trinnex. He has 15 years of experience as a Product Leader and Solutions Consultant. He is experienced in project planning, process and requirements analysis, stakeholder engagement and development of workflows that evolve into highly detailed systems. He has spent the past four years immersed in LCRR technology projects.

Trinnex's team of engineers and data scientists can walk you through how Capacity Planner's advanced algorithms and digital twin can help you make data-backed decisions on your sewer capacity and provide an end-to-end solution for in-house sewer permit management.

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