Kyong Song
Sr. Environmental Engineer
Barr Engineering Co., Minneapolis, MN
ksong@barr.com
Presentation Date: September 28, 2023
With escalating concerns around soil and groundwater contamination, there is an increased need to address issues around contamination and remediation with well-informed data-driven practices. Complexities can arise in identifying distinct chemical signatures across varying geographies and mixed plumes from multiple contamination sources. This presentation aims to demonstrate practical use cases that leverage machine learning methods to enhance the identification of chemical signatures along 3D spatial coordinates of latitude, longitude, and depth.
Machine learning methods can work effectively with high-volume dimensional data, which can extract complex patterns and trends that might be otherwise neglected. The outcome of these techniques includes improved accuracy for identifying chemical signatures and greater understanding of spatial and geological distribution and impact. These insights can then drive more focused and effective remediation campaigns.
Due to the sensitive nature of matters regarding contamination, specific client information will not be published or referenced for this presentation. Instead, the focus will be on the machine learning use cases that have been utilized during these projects and will leverage publicly available datasets for data analysis examples and visualizations.
BIO:
Kyong’s expertise lies at the intersection of environmental engineering and data science. He has over ten years of experience in EHS and digital transformation, collaborating with organizations across various sectors to achieve their environmental, sustainability, and technology goals. He is currently a Senior Environmental Engineer with Barr Engineering, focusing on projects related to environmental management information systems, data science, and digital transformation strategy. He has an MS in Environmental Engineering, BS in Biomedical Engineering, and is currently an MS candidate in Data Science.