High Dimensional Data Analysis and Visualization to Demonstrate Compliance.pdf

Alex Eklund, TRC

Description

High Dimensional Data Analysis and Visualization to Demonstrate Compliance Authors Ms. Alex Eklund - United States - TRC Abstract Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are powerful statistical techniques that can be used to evaluate high dimensional groundwater data generated throughout coal combustion residual (CCR) monitoring programs. These techniques are particularly useful in assessing unanticipated changes in groundwater quality and distinguishing sources of CCR impacts. These methods offer several benefits in the analysis and interpretation of data through: 1) dimensionality reduction and visualization, reducing the dimensionality of the dataset to more easily visualize and make correlations in the data; 2) dominant pattern identification, revealing which variables are the dominant factors influencing groundwater quality differences; 3) noise reduction, focusing on the significant components to identify meaningful patterns and relationships; and 4) multivariate analysis, where the relationships among multiple variables are taken into account simultaneously. The multivariate analysis capability is a key benefit for groundwater analysis as it accounts for the interdependencies among various parameters (e.g., pH, chemical concentrations, temperature) that affect water quality, particularly for large-volume datasets. These methods provide quantitative analysis to aid in making more informed decisions and developing effective strategies for groundwater management and remediation. TRC’s presentation will provide valuable insight into these statistical techniques along with real-world CCR program application.

 
May 16th, 11:00 AM May 16th, 11:30 AM

High Dimensional Data Analysis and Visualization to Demonstrate Compliance.pdf

Grand Rapids, Michigan

High Dimensional Data Analysis and Visualization to Demonstrate Compliance Authors Ms. Alex Eklund - United States - TRC Abstract Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are powerful statistical techniques that can be used to evaluate high dimensional groundwater data generated throughout coal combustion residual (CCR) monitoring programs. These techniques are particularly useful in assessing unanticipated changes in groundwater quality and distinguishing sources of CCR impacts. These methods offer several benefits in the analysis and interpretation of data through: 1) dimensionality reduction and visualization, reducing the dimensionality of the dataset to more easily visualize and make correlations in the data; 2) dominant pattern identification, revealing which variables are the dominant factors influencing groundwater quality differences; 3) noise reduction, focusing on the significant components to identify meaningful patterns and relationships; and 4) multivariate analysis, where the relationships among multiple variables are taken into account simultaneously. The multivariate analysis capability is a key benefit for groundwater analysis as it accounts for the interdependencies among various parameters (e.g., pH, chemical concentrations, temperature) that affect water quality, particularly for large-volume datasets. These methods provide quantitative analysis to aid in making more informed decisions and developing effective strategies for groundwater management and remediation. TRC’s presentation will provide valuable insight into these statistical techniques along with real-world CCR program application.