A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

Atmospheric Measurement Techniques

Volumn 11, Issue 1, 2018, Pages 291-313

  Zimmerman, Naomi ^a   Presto, Albert A ^a   Kumar, Sriniwasa P N ^a   Gu, Jason ^b   Hauryliuk, Aliaksei ^a   Robinson, Ellis S ^a   Robinson, Allen L ^a   Subramanian, R ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b Sensevere LLC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIR QUALITY; ATMOSPHERIC POLLUTION; CALIBRATION; DATA QUALITY; DATA SET; ENVIRONMENTAL CONDITIONS; MACHINE LEARNING; MODEL; OXIDE; OZONE; PERFORMANCE ASSESSMENT; POLLUTION EXPOSURE; POLLUTION MONITORING; PRECISION; SENSOR;

PENNSYLVANIA; PITTSBURGH; UNITED STATES;

EID: 85045292949     PISSN: 18671381     EISSN: 18678548     Source Type: Journal    
DOI: 10.5194/amt-11-291-2018     Document Type: Article

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