C+S March 2020 Vol. 6 Issue 3 (web)

MACHINE LEARNING IN MINERAL WELLS City Uses Novel Techniques to Determine Stormwater Utility Rates By Tak Makino

West of the Dallas-Fort Worth metroplex is a small city of approxi- mately 15,000 people called Mineral Wells. Over the last decade, the city has been battered by repetitive flooding events that have strained its existing stormwater infrastructure. Facing a budget shortfall and an urgent need to upgrade its stormwater infrastructure, the City contract- ed Lockwood, Andrews & Newnam, Inc. (LAN), a national planning, engineering, and program management firm, and NewGen Solutions, a management consulting firm, to perform a stormwater utility fee study. Based on the study, the city wanted to set new storm water utility rates that would subsequently be used to expand its stormwater infrastructure. Stormwater Utility Fee Components Three major components go into a stormwater utility fee study: Utility billing data provides a convenient existing framework upon which to build the stormwater utility fee. The stormwater fee can be added as an additional charge item to existing utility customers. This avoids the creation of a new billing system for the stormwater utility fee and allows both customers and the city to use a billing system with which they are familiar. The second essential component is the parcel data. For the Mineral Wells study, LAN obtained the parcel data from the Palo Pinto and Parker County Appraisal Districts. The parcel GIS files define the geographic zone of responsibility for each utility billing customer. Utility billing customers are responsible for paying for the runoff their property contributes to the stormwater utility system. In the case of multiple utility billing customers on a single parcel (e.g. apartments or duplexes), the total parcel impervious surface is divided by the number of utility billing customers such that each billing customer pays for an equitable share of the runoff that enters the stormwater system. The last, and perhaps the most critical piece, is the impervious sur- face coverage. Aerial imagery from the National Agricultural Imagery Program (NAIP) formed the basis of the impervious surface analysis. NAIP imagery provides four-band (red, green, blue, and near-infrared) aerial imagery at 0.5m resolution, meaning that each pixel in the aerial imagery represents a 0.5m x 0.5m area on the ground. Within each pixel, four values are stored – a value each for red, green, blue, and near infrared wavelengths (see figure 1). Luminosity on the red, green, and 1. Utility billing 2. Parcel data 3. Impervious surface coverage

In addition to the red, green, and blue bands of color imagery, the four-band aerial imagery used in this study includes a fourth infrared band. Photo: LAN

Comparison between full color imagery (left) and color infrared (right). Infrared data provides a fourth variable for analysis. Photo: LAN

blue wavelengths produce a true-color, composite image, much like how the eye sees. The near infrared band provides a fourth dimension that allows for the discrimination of otherwise spectrally similar land cover classes. For example, aerial imagery of trees and grass – both green – can appear similar when examining a red, green, blue compos- ite image. Using a statistical, value-based method of analysis (rather than by visual observation), it would be difficult, if not impossible, to discriminate between trees and grass from three-band aerial imagery alone. However, trees and grass reflect the infrared wavelengths differ- ently, allowing for a statistically meaningful, value-based separation of trees and grass. These three components – the utility billing, land parcels, and aerial imagery – need to be combined into a single database, one that reports the amount of impervious surface for which each utility billing cus- tomer is responsible. A combination of methods was used to achieve this goal. The first step taken was to identify areas of impervious sur- face coverage using machine learning algorithms. Machine Learning Supervised machine learning is a technique in which an operator pro- vides a training dataset to the computer, in this case user-defined areas of different types of land cover, and the algorithms then learn what characteristics define the classes in the training dataset. For example, numerous examples of paved surfaces are provided, along with the declaration that the provided examples are pavement, to the extent that the computer is then able to correctly identify a never-before-seen area of pavement as pavement. In other words, given enough examples, the computer learns what characteristics define pavement.


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