PAWSC Ecotoxicology PFAS Machine Learning [2024 INV#WO0000000112908]
Description
assess the ecological health risk of PFAS in Pennsylvania stream surface water
Detailed example
Leveraging a tailored convolutional neural network (CNN), a validation accuracy of 78% was achieved, directly outperforming traditional methods that were also used, such as logistic regression and gradient boosting (accuracies of 65%)
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
predict potential PFAS exposure effects in unmonitored stream reaches
Controls / human review
ATO: No; PIA: Not published
Data needed
PFAS concentrations in environmental waters, specifically streams for this model.