Pollutant Data Prediction: Protecting our Watershed Through Sensing – UROP Spring Symposium 2021

Pollutant Data Prediction: Protecting our Watershed Through Sensing

Gina Kittleson


Pronouns: She/Her

Research Mentor(s): Branko Kerkez, Assistant Professor
Research Mentor School/College/Department: Civil and Environmental Engineering, College of Engineering
Presentation Date: Thursday, April 22, 2021
Session: Session 6 (4pm-4:50pm)
Breakout Room: Room 18
Presenter: 1

Event Link


Watershed health largely influences quality of life, but it is difficult to quantify. Tracking and reducing pollutants is one way to improve watershed health. Total suspended solids (TSS) is an invaluable measurement in determining the amount of pollutants in a body of water. Despite many studies relating TSS to turbidity (water opacity) or doppler backscatter (sound refraction), there is yet to be a way to accurately predict TSS on a long term basis. This study takes previously calculated correlations between suspended solids concentration (SSC) and doppler backscatter using linear regression and conducts a gaussian process on the data to determine a more accurate model. The model created is based on probabilistic computations with a 95% confidence interval. This model will require initial data points from a site before a model can be developed, unlike the previous model, but the confidence interval will decrease in size as more data points are added. Therefore, we will be able to determine a more accurate model for each specific site. The model developed in this study will then be used to predict TSS values for new sites as TSS and SSC are related measurements, and TSS is a more widely used measurement in regulations. This result allows us to continue with further research into the number of true TSS/ SSC samples necessary to determine an accurate model and how the model will react to changes in flow rate implemented by controlled valves.

Authors: Gina Kittleson, Meagan Tobias, Branko Kerkez
Research Method: Computer Programming

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