Get Full Government Meeting Transcripts, Videos, & Alerts Forever!
Get email alerts on the Stormwater topic
No spam. Unsubscribe anytime.
City staff recommend terminating Army Corps contract, contracting NEAR Technologies for stormwater master plan
Summary
Staff told the committee that missing critical field elevations for about 900 assets made continuation with the Army Corps impractical; staff recommended terminating that agreement, seeking an estimated $150,000 refund, and awarding a roughly $65,000 contract to NEAR Technologies to deliver a dynamic, cloud-based stormwater master plan and dashboard.
Get email alerts on the Stormwater topic
No spam. Unsubscribe anytime.
Stephanie Boyce told the committee that the city entered a Planning Assistance to States agreement with the U.S. Army Corps of Engineers but discovered a large data gap (rim elevations and pipe depths for roughly 900 assets) that would either force costly field surveys or reduce model quality. "Based on the evaluations and discussions between the Army Corps and staff, we determined that continuing under the current agreement was not a viable option," Boyce said.
She presented NEAR Technologies as an alternative vendor that will combine existing GIS and as-built data with advanced data cleaning, hydrology/hydraulic modeling, and a cloud-based dashboard to produce an updatable, dynamic master plan. Boyce said the city's share of the original Corps contract was $178,000, estimated refundable unused funds were around $150,000, and the NEAR contract proposal was about $65,000, which would reduce overall city costs while delivering a usable, evolving toolset for prioritizing projects and updating models.
NEAR's representative (identified in the packet) explained the technical approach: populate missing pipe sizes and structure elevations using local data and machine-learning models trained on the city's own GIS and historical as-built records; provide 1D (pipe) and 2D (surface/neighborhood) modeling and a prioritization dashboard for capital projects. Committee members asked about accuracy and safeguards for machine-learning fills; NEAR said typical accuracy ranges from 85%–95% depending on data quality and that the deliverable will include recommended projects and a dynamic platform staff can update over time. The committee recommended terminating the Army Corps agreement and awarding the NEAR contract for consideration on the consent/regular agenda.

