Machine Learning Facilitated Stormwater Financing (Source: Forester University Webinar)

By Tak Makino, Chris Ekrut and Michael Sommerdorf

The control of excessive runoff and flooding is a public good that must be provided for the safety and well-being of the populace. Such activities require significant monetary support to achieve needed capital improvements.

To generate needed revenue, many entities enact stormwater utility fees, which are monthly-collected fees paid by utility billing customers for stormwater services. Typically, a stormwater utility assesses a fee proportional to the amount of measured impervious area on a property. This measurement allows for the fee to be levied in a manner proportional to amount of stormwater service consumed and to meet the legal basis for such fees.

However, the information required to develop a stormwater utility fee can be complex and unwieldy.

Join Forester University for this live, educational webinar as speakers Tak Makino (CRS/Flood Mitigation Manager, LAN), Chris Ekrut (CFO, NewGen), and Michael Sommerdorf (Senior Consultant, NewGen) discuss how employing machine learning and other automation techniques can reduce the cost and time required to enact a stormwater utility fee.