FOCUS on Contamination: A Geospatial Deep Learning Framework with a Noise-Aware Loss for Surface Water PFAS Prediction

• URL: http://arxiv.org/abs/2502.14894v1
• Date: Mon, 17 Feb 2025 16:57:10 GMT
• Title: FOCUS on Contamination: A Geospatial Deep Learning Framework with a Noise-Aware Loss for Surface Water PFAS Prediction
• Authors: Jowaria Khan, Alexa Friedman, Sydney Evans, Runzi Wang, Kaley Beins, David Andrews, Elizabeth Bondi-Kelly,
• Abstract summary: FOCUS is a deep learning framework with a label noise-aware loss function to predict PFAS contamination in surface water over large regions.<n>We integrate hydrological flow data, land cover information, and proximity to known PFAS sources to improve prediction accuracy.<n>Results highlight our framework's potential for scalable PFAS monitoring.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Per and polyfluoroalkyl substances (PFAS), chemicals found in products like non-stick cookware, are unfortunately persistent environmental pollutants with severe health risks. Accurately mapping PFAS contamination is crucial for guiding targeted remediation efforts and protecting public and environmental health, yet detection across large regions remains challenging due to the cost of testing and the difficulty of simulating their spread. In this work, we introduce FOCUS, a geospatial deep learning framework with a label noise-aware loss function, to predict PFAS contamination in surface water over large regions. By integrating hydrological flow data, land cover information, and proximity to known PFAS sources, our approach leverages both spatial and environmental context to improve prediction accuracy. We evaluate the performance of our approach through extensive ablation studies and comparative analyses against baselines like sparse segmentation, as well as existing scientific methods, including Kriging and pollutant transport simulations. Results highlight our framework's potential for scalable PFAS monitoring.

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