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Investigation of Performance of Tropospheric Ozone Estimations in The Industrial Region Using Differential Artificial Neural Networks Methods

  • Authors (legacy)
    Corresponding: Andac Akdemir
    Co-authors: Akdemir A., Filiz B. and Özel Akdemir Ü.
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  • gnest_02328_published.pdf
  • Paper ID
    gnest_02328
  • Paper status
    Published
  • Date paper submitted
  • Date paper accepted
  • Date paper online
DOI
https://doi.org/10.30955/gnj.002328
Abstract

The method of Levenberg-Marquardt learning algorithm was investigated for estimating tropospheric ozone concentration. The Levenberg-Marquardt learning algorithm has 12 input neurons (6 pollutants and 6 meteorological variables), 28 neurons in the hidden layer, and 1 output neuron for the Ozone (O3) estimate. The Multilayer Perceptron Model (MLP) performance was found to make good predictions with the mean square error (MSE) less than 1 µg/m3 (0.002 µg/m3). In addition, the correlation coefficient ranges from 0.74 to 0.95 in The Levenberg-Marquardt learning. The Levenberg-Marquardt learning algorithm that a multilayer perception method of Artificial Neural Network (ANN) has performed well and an effective approach for predicting tropospheric ozone. Ozone concentration was influenced predominantly by the nitrogen oxide (NOx, NO2, NO), SO2 and temperature. The model did not predict solar radiation to ozone with sufficient accuracy.

Keywords
MLP Levenberg-Marquardt Ozone ANN
Cite this article
Akdemir, A., Filiz, B. and ?zel, A. (2018) “Investigation of Performance of Tropospheric Ozone Estimations in The Industrial Region Using Differential Artificial Neural Networks Methods”, Global NEST Journal, 20(1). Available at: https://doi.org/10.30955/gnj.002328.
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  • gnest_02328_published.pdf