ASDWA and BlueConduit publish white paper on data science for LSL inventory and replacement
In light of the importance of lead service line inventories expected in the final Lead and Copper Rule Revisions, BlueConduit partnered with ASDWA to develop a white paper that outlines important considerations for state regulators and utility leadership when using statistical and predictive methods for LSL inventory and replacement.
Substantial uncertainty still surrounds the nation’s water systems regarding the number and locations of lead service lines (LSLs). The kind of uncertainty that the LSL question presents is well-suited for data science methods that have evolved in recent years. Given the significant public health, regulatory, and financial implications of these decisions, it is essential that regulators and utilities be aware of and adhere to some fundamental statistical methods when using predictive methods to inform SL work. Download the whitepaper here
The paper includes five guiding principles for using data science to better characterize uncertainty LSL inventories:
- Clean data management and organization;
- Not accepting all historical records as truth;
- Conducting a representative randomized sample of service lines;
- Transparency in public outreach and reproducibility; and
- Accuracy on held-out sample.
These principles can be used by regulators to encourage water systems to plan strategically, make data-driven decisions, set budgets and requests for funds, build capacity in some skill areas, communicate with the public and build trust, and, most importantly, continue to protect the health of all individuals in the system.
The white paper draws on the BlueConduit team’s experience in Flint where their statistical machine learning algorithm was used to guide pipe replacements.
For more information about BlueConduit and how we work with communities to reduce uncertainty around LSL inventory and replacement, connect with us at firstname.lastname@example.org. You can also find us on Twitter at @BlueConduitAI