A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions

Science of the Total Environment

Volumn 651, Issue , 2019, Pages 475-483

  Kamińska, Joanna A ^a  

^a WROC AW UNIVERSITY OF ENVIRONMENTAL AND LIFE SCIENCES   (Poland)

Author keywords

Meteorological conditions; Nitrogen dioxide concentration; Random forest; Traffic flow; Urban air pollution

Indexed keywords

ATMOSPHERIC HUMIDITY; DATA MINING; DECISION TREES; ITERATIVE METHODS; LEARNING SYSTEMS; NITROGEN OXIDES; POLLUTION;

METEOROLOGICAL CONDITION; NITROGEN DIOXIDES; RANDOM FORESTS; TRAFFIC FLOW; URBAN AIR POLLUTION;

ATMOSPHERIC MOVEMENTS;

NITROGEN DIOXIDE;

ATMOSPHERIC MODELING; ATMOSPHERIC POLLUTION; CONCENTRATION (COMPOSITION); METEOROLOGY; NITROGEN DIOXIDE; PREDICTION; ROAD TRAFFIC; URBAN AREA;

ARTICLE; ATMOSPHERIC DEPOSITION; CONCENTRATION (PARAMETERS); DATA MINING; DECISION TREE; ENVIRONMENTAL TEMPERATURE; HUMIDITY; MACHINE LEARNING; MEASUREMENT ACCURACY; METEOROLOGY; POLAND; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; TRAFFIC AND TRANSPORT; WIND SPEED;

POLAND [CENTRAL EUROPE]; WROCLAW [POLAND];

EID: 85053787898     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2018.09.196     Document Type: Article

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