Enhancing Low-Cost Sensor Reliability for Air Quality Monitoring using Deep Learning and Monte Carlo-based Uncertainty Quantification

Jaddu, Sidh

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Abstract: Enhancing Low-Cost Sensor Reliability for Air Quality Monitoring using Deep Learning and Monte Carlo-based Uncertainty Quantification (AGU25)

Enhancing Low-Cost Sensor Reliability for Air Quality Monitoring using Deep Learning and Monte Carlo-based Uncertainty Quantification

Sidh Jaddu, Thomas Jefferson High School for Science and Technology, Fairfax, VA, UNITED STATES, Kaylee Smith, University of Michigan, Ann Arbor, United States and Chaowei Phil Yang, George Mason Univ., Fairfax, United States