The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 483–488, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-483-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 483–488, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-483-2017

  27 Sep 2017

27 Sep 2017

TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

A. Shamsoddini, M. R. Aboodi, and J. Karami A. Shamsoddini et al.
  • Department of RS and GIS, Tarbiat Modares University, Tehran, Iran

Keywords: AIR POLLUTION, RANDOM FOREST FEATURE SELECTION, ARTIFICIAL NEURAL NETWORKS, MULTIPLE-LINEAR REGRESSION, HUMAN HEALTH, TEHRAN

Abstract. Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.