arXiv AI June 11, 2026 By Jowaria Khan, Alexa Friedman, Sydney Evans, Rachel Klein, Runzi Wang, Katherine E. Manz, Kaley Beins, David Q. Andrews, Elizabeth Bondi-Kelly

FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping

Read the original on arXiv AI →

arXiv:2502. 14894v5 Announce Type: replace-cross Abstract: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

computer-vision

arXiv AI

Jul 29

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations

arXiv:2607. 25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making.

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diffusion benchmarks

arXiv Machine Learning

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rag multimodal benchmarks

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

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By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

safety

arXiv Machine Learning

Jul 20

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rag multimodal safety

arXiv AI

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arXiv:2604. 26051v2 Announce Type: replace-cross Abstract: The increasing number of satellites has improved the temporal resolution of Earth observation, making satellite-based flood mapping a promising approach for operational flood monitoring.

By Hyunho Lee, Wenwen Li

safety

arXiv Machine Learning

Jul 23

Predicting Groundwater Arsenic Concentrations Using Graph Neural Networks

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By William Xing, Stephanie Yang, Aarush Bandemegal, Anushree Misra, Ananya Kalapatapu, Brennan Lagasse, Kevin Zhu

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